PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans
Authors: Giang Nguyen
Valerie Chen
Mohammad Reza Taesiri
Anh Totti Nguyen
Venue: Accepted by TMLR ()

Abstract: Nearest neighbors (NN) are traditionally used to compute final decisions, e.g., in Support Vector Machines or k-NN classifiers, and to provide users with explanations for the model's decision.
In this paper, we show a novel utility of nearest neighbors: To improve predictions of a frozen, pretrained image classifier C. 
We leverage an image comparator S that (1) compares the input image with NN images from the top-K most probable classes given by C; and (2) uses scores from S to weight the confidence scores of C to refine predictions.
Our method consistently improves fine-grained image classification accuracy on CUB-200, Cars-196, and Dogs-120.
Also, a human study finds that showing users our probable-class nearest neighbors (PCNN) reduces over-reliance on AI, thus improving their decision accuracy over prior work which only shows only the most-probable (top-1) class examples.

Published in Transactions on Machine Learning Research (08/2024)
PCNN: Probable-Class Nearest-Neighbot Explanations Im- prove Fine-Grained Image Classification Accuracy for Als and Humans
Giang (Dexter) Nguyen
aguyenggingghhuu@@mmaaac.mmm
Computer Science and Software Enngineerng Depertment
Auburn University
Valerie Chen
vchen22endree.come edu
Machine Learning Deportment Cornegie Mellon Universily
Mohammad Reza Tacsiri
mtaesiri@gmmailcomm com
Electrical & Computer Engineering Department University of Alberta
Anh Totti Nguyen
anh. Boh.ngg@@gmart @graail com
Computer Science and Software Engineering Department
Aubnrn University
Reviewed on OpenReview: https: // openreview. net/ forum? id= (d=
Abstract
Nearest neighbors (NN) are traditionally used to compute final decisions, c.g., Support Vector Machines or k-NN classifiers, and to provide users with explanations for the model's decision. In this paper, show novel utility of nearest neighbors: To improve predictions of frozen, pretrained image classifier C. We leverage image comparator S that (1) compares the imput image with NN images from the top-K most probable classes given by C: and (2) uses scores from S to weight the confidence scores of C to refine predictions. Our method consistently improves fine-grained image classification accuracy on CUB-200, Cars- 196, and Dogs- 120. Also, human study finds that showing users our probable-clase nearest neighbors (PCNN) reduces over-reliance on AI, thus improving their decision accuracy prior work which only shows only the most-probable (top-1) class examples.
1 Introduction
k-nearest neighbors are traditionally considered explainable classifiers by design Papernot McDaniel 2018 Yet, only recent. human studies have found concrete evidence that showing the NNs humans improves their docision-making accuracy (Nguyen et al.
2021 Liu et al.] 2022 Chen
2023b Chan et al. 2023 Kenny even more ellectively than feature attribution
eet al. 2022 2023 Chiaburu et al. 2024 Nguyen et al. 2024
in the image domain [Nguyen et al. 2021 Kim et al. 2022 These studies typically presented users with
the input image, model's top-1 prediction and the NNs from the top-1 class. However, examples from the top-1 class are not always beneficial to users.
One such setting where top-1 neighbors can actually hinder human decision-making accuracy is in fine- Enne-grained image classification. When users are asked accept or reject the model's decision-4 distinction task, as shown in Fig. [a), examples from the top-1 class easily fooled users into incorrectly accepting wrong predictions at excessively high rate (e.g., rate of 81.5% CUB-200; Table A6 in Taesiri et al. (2022))

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Published in Transactions on Machine Learning Research (08/2024)
(a) prior work
(b) extra probable-class nearest neighbors improve
Input
top-1
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Caspian
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64%
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31%
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Figure 1: Given an input image x and a black-box, pretrained classifier C that predicts the label for æ. Prior work (a) often shows only the nearest neighbors from the top-1 predicted class as explanations for the decision, which often fools humans into accepting wrong decisions (here, Caspian Tem) due to the similarity between the imput and top-1 class examples. Instead, including extra nearest neighbors (b) from top-2 to top-K classes improves not only human accuracy this binary distinction task but also Al's accuracy standard fine-grained image classification tasks (see Fig. 2
This is because NNs from the top-1 class often looked doccivingly similar to the input (o.g., input vs. Caspian; Fig.
Instead of focusing on top-1 examples, we propose to sample the NN examples from the top-K predicted classes to more fully represent the query and better inform users to make dccisions. That is, we propose Probable-Clase Class Nearest Neighbors (PCNN), novel explanation lype consisting of K nearest images, where each image is taken from class among the top-K classes, as illustrated in Fig, [b). We show that PCNN not only improves human decisions on the distinction task over showing top-1 class neighbors but can also be leveraged to improve Al-alone accuracy by re-ranking the predicted labels pretrained, frozen classifier (Fig.
Assumptions As is the case for many real-world applications, we assumee that there exists a pretrained, black-box classifier C, e.g., foundation model (Bommasani et al. 2021 responsible for large amount of information processing in the pipeline. Due to computation and algorithm constraints, C may not be easily
re-t re-trained to achieve better accuracy. Therefore, like Bansal et al. 2202] we assume that C is frozen humans or other models would interact with C to make final decisions (Fig.
To leverage PCNN for re-ranking C's predicted labels, we train an image comparator S, which is a binary classifier that compares the input image with each PCNN example and outputs a sigmoid value that usod to weight the original confidence scores of C (Fig. 2 Then, C and S together form C S model-llkee Product of Experts Hinton (1999) that outperforms C alone.
Our experiments on 10 different C classifiers across 3 fine-grained classification tasks for bird, car, and dog
species reveal?
Our PCNN-based model consistently improves upon the original accuracy on all three domains: CUB-200, Cars-196, and Dogs-120 (Sec. 4.1).
Given the same ResNet backbones, our model outperforms similar explainable, prototype-based classifiers including k-NN, part-based, and correspondence-basel classifiers on all three datasets
(Sec. 4.5
1 Codee and data available https://github. http:://gghhai..ccmm/aaaaaaaaaa/ htt.://www...o///a////////aaaaaa
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Intercstingly, even without further training the comparator S new pretrained C-wee still obtain large gains (up to +23.38 points) when combining well-trained comparator S with an arbitrary C model (Sec. 4.4p.
60-user study finds that PCNN explanations, compared with top-1 class examples, reduce over- reliance AI, thus improving performance the distinction task by almost 10 points (54.55% vs. 64.58%) CUB-200 (Sec. 4.6).
2 Related Work
Example-based explanations on the distinction task
The distinction task (Fig. 1 was introduced in
prior studies Nguyen et al. (2021): : Kim et al. (2022): Fel et al. 2023 Colin et al. 2022 to test the utility of
an explanation method. Yet, many works Nguyen et al. (2021): Taesiri et al. (2022
Kim et al. 2022 Kenny Jeyyauuaa et al. (2020); Chen et al. (2023b); Nguyen et al. (2024) showed users examples from
ctt al. (2023): Jeyakumar only the lop-l class, potentially limiting the utility of nearest. neighbors and user accuracy. We find that,
given the same budget of five examples, human distinction accuracy substantially improves if each example comes from a unique class among the top-5 predicted classes (Sec. 4.6 To our knowledge, our work is the first to report the benefit of class-wisc contrastive examples to users' decision-making.
Re-ranking for image classification Re-ranking is common in image retrieval Phan Nguyen (2022 ;:
Zhang et al. (2000); Li et al. (2023). For image classification, re-ranking the nearest neighbors of a k-NN image classifier can improve its classification accuracy Tacsiri al. (2022). Yet, here, we re-rank the top-K predicted labels originally given by a classifier C.
Ensembles for image classification Our method for combining two separate models (C and S) adds to the long literature of model ensembling. Specifically, our method of sampling hard, negative pairs of images using C's predicted labels to train the image comparator S is akin to boosting [Schapire (2003). That is, S is trained with the information of which negative pairs are considcrod "hard" (i.c., very similar images) according to the approach we find more effective than random sampling (Appendix B.4]. While boosting aims train set of classifiers for the exact same task, our C is a standard maan--ay image classifier and S is binary classifier that compares two input images.
Our combination of C and S is also inspired by PoE Hinton 1999 which train both experts at the same time and for the same task, here C and S are two separate classifiers with diffcrent input and output structures. A key difference from standard PoE and boosting tochniques is that we leverage training-set examples (PCNN) at the test time of S, improving C x S model accuracy further over the baseline C. Also, our model does not strictly follow the PoF. framework's requirement of conditional independence between experts because the confidence scores that image compartor S assigns to most-probable classes can be influenced by the initial ranking from C.
Unlike the traditional PoE algorithms,
Prototype-based image classifiers Many prototype-based classifiers Chen et al. (009 Taesiri et al. (002)) operate at the patch level. Instead, our classifier operates at the image level, similar to K-NN classifiers Nguyen et al. (2021). While most prior prototype- based classifiers are single models, we combine two models (C and S) into one, Furthermore, the scores given by S enable an interpretation of how original predictions are ro-ranked (Fig. 2
3 Methods
3.1
Tasks
Task 1: Single-label, many-way image classification Let C be a frozen, pretrained image classifier that takes in an image æ and outputs softmax probability distribution over all c possible classes, e.g., c= 200 for CUB-200 Wah al. (2011). Let S be an image comparator that takes in two images and outputs sigmoid score predicting whether they belong to the same class (Fig. 3) Our goal is to improve the final classification accuracy without changing C by leveraging a separate image comparator network and PCNN (Fig. 2
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Caspian
Elegant
3. Least
4. Foster's
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K Probable-Class Nearest Neighbors (PCNN)
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Re-ranking by weighting confidence
S
i.e. C(x) S(z, nn) scores
classifier
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0.33
31%
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C
31%
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0.99
21%
Caspian Tern X
2%
Least Tern 0.02
0%
Least Tem
1%
Forster's Tern x
0.00
0%
Forster's Tern
Figure 2: C x S re-ranking algorithm: From each class among the top-K predicted classes by C, we find the nearest neighbor nn to the query x and compute a sigmoid similarity score S(r, which weights the original C(x) probabilities, re-ranking the labels. See Algorithm for the written algorithm.
Task 2: Distinction task for humans Following Nguyen et al. (2021 Tacsiri et al. 2022 we provide each user with the imput query, the top-1 prediction given by C, and an explanation (e.g., five PCNN images; Fig. and ask the user to accept or reject the top-1 predicted label.
3.2 Datasets and pretrained classifiers
We train and test our method on three standard fino-grained image classification datasots of birds, cars, and dogs. To study the generalization of our findings, we test total of 10 classifiers C (4 bird, 3 car, and 3 dog classifiers) of varying architectures and accuracy.
CUB-200 (CUB-200-2011 Wah et al. (2011) has 200 bird species, with 5,994 images for training and 5,794 for lesting (samples in Fig. 5al. We test four different classifiers: a ResNet-50 pretrained on iNaturalist Van Horn et al. 2018) and finetuned on CUB-200 (85.8%% accuracy) by Taesiri et al. (022 : and three ImageNet-
pretrained ResNets (18, 34, and 50 layers) finetuned on CUB-200 with 60. 20. 62.81%, and 62.98% accuracy, respectively.
Cars-196 (Stanford Cars) Krause al. 2013) includes 196 distinet classes, with 8,144 images for training and 8,041 for testing (samples in Fig. 5b We use ResNet-18, ResNet-34, and ResNet-50, all pretrained on ImageNet and then finetuned on Cars-196. Their top-1 accuracy scores are 86.17%, 82.99%, and 89.73%, respectively.
Dogs-120 (Stanford Dogs) Khosla et al. [0011 has a total 120 dog breeds, with 12,000 images for training and 8,580 images for testing (samples in Fig. 5c). We test three models: ResNet-18, ResNet-34, and ResNet-50, all pretrained on ImageNet. and then finetuned Dogs-120, achieving top-1 accuracy of 78.75%, 82.58%, and 85.82%.
3.3 Re-ranking using both image comparator S and classifier C
PONN is a set of K nearest-neighhor images to the query where each image is taken from one training-set class among the top-K predicted classes by C (see Fig. We empirically test K= 3,,,,3,5, 10, 15) in Appendix B.3 and find 10 to be optimal.
The distance metric for finding nearest neighbors per class Lg (using faiss framework) Johnson al. (019)) at the average pooling of the last conv features of C.
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Patch token
sigmoid
parrs
pairs
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Elegant
Forster's
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GT: Elegant
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ys
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coav]
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20%
Casplan Tem
top-3
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Q
+ pairs
pairs
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1% Forster's Tem
Figure 3: Our comparator takes in pair of images (æ, nn) and outputs sigmoid score s S(x, nn) [0, 1] in- dicating whether two images belong to the same class. L, M, and are the depths of the respective blocks.
Figure 4:
For each training-set image æ, sample Q nearest images
from the groundtruth class of x to form Q positive pairs [(x, ) To sample Q hard, negalive pairs: Per non-groundtruth class among the top-Q predicted classes from C(z), we take the nearest image to the input. Here, when the groundtruth label (Elegant Tem) is among the top-Q labels, there would be only Q negative pairs.
Re-ranking algorithm Given well-trained comparator and the obtained PCNN, we repeat. the following for each class among the top-K classes: Multiply each original confidence score in C(x) by corresponding score ((x,n)) where nn is the nearest ncighbor from corresponding predicted class (sce Fig. 2
Based on the newly weighted scores C(x) x S(x, nm), we re-rank the top-K labels. See Fig. for examples of how re-ranking corrects originally wrong predictions by C.
3.4
The architecture and training of comparator S
3.4.1
Network architecture
Our image comparator S follows closely the design of the CrossViT (Chen al. 2021). which takes in a
pair of images (sce Fig. 3) The image patch embeddings are initialized with convolutional features from pretrained convolutional network (here, we directly use the classifier C for convenience but such coupling is not mandatory; see Sec. 4.4
Details is the subnetwork up to and including the last conv layer of a given classifier C. For example, we train a comparator S for ResNett-50, would be layer4 per ToyTo definition or conv5 in He et al. (2016). Using the pretrained feature extractor f, we extract two conv emboddings Xcouv1 and Xconv2 Dxx where H. W are spatial dimensions and D is the depth. We then flatten and transpose these embeddings to shape them into T (e.g., T H W 49 and D = 2048 for ResNet-50). These embeddings subsequently prepended with the CLS token Xats LxDx and added to the learned positional embedding Xpos (I+TT)> After that, the two image embeddings X1 and X2 e (1+T)x> are fed into the first Transformer block that consists of N MHSA layers and M CrossAttenting layers (Fig. We repeat. the Transformer block times.
MHSA is the multi-headed sclf-attention operator Vaswani al. (0177) and Cross Attention refers token fusion block in CrossViT Chen et al. (2021). These two blocks produce y1, Y2. the (I+T)),) respectively (Fig. 3). Next, concatenate the two CLS tokens from 2 and Z2 and 21, 22
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input for 4-layer MLP. Each layer uses GeLU Hendrycks & Gimpel Szegedy (2015), except the last layer. The output of the MLP is a sigmoid value that indicates whether a and nn belongs to the same class. Full architecture details are in Appendix
(0166) and Batch Normalization loffe &
3.4.2 Training the comparator
Objectives We aim to train S to separate image pairs taken in the same class from those pairs where images are from two different classes. As standard in contrastive learning Chen et al (2020), first construct set of positive pairs and set of negative pairs from the training set, and then train S using a binary sigmoid cross-entropy loss. Note that training the comparator also finetunes the pretrained conv layers f, which are part of the comparator model (Fig. 3)
A Augmentation Bocause the three tested fino-grained classification datasets are of smaller sizes, we apply TrivialAugment (Mäller & Hutter 2021 to image pairs during training to reduce overfitting. For image pre-processing, we first resize images that their smaller dimension is 256, and then take center crop of 224 x 224 from the resized image.
Optimization For all three datasets, we train comparators S for 100 epochs with batch size of 256 using Stochastic Gradient Descent (SGD) optimizer with 0.9 momentum and the OneCycleLR Smith & Topin 2019 learning-rate scheduler.
Some hyperparameters including learning rates vary depending on the datasets. See Appendix A for more training details including optimization hyperparameters, data augmentation, and training loss.
3.4.3
Sampling positive and negative pairs
For each training-set example x, we construct a set of positive pairs {(x, nn4)) and negative pairs (Fig. To find nearest images, we use the distance metric described in Sec. 3.3.
positive pairs We take Q nearest images nn4 the query æ from the same class of x (e.g., Elegant Ter in Fig. 4
negative pairs One can also take un. nearest images from the random non-groundtuuth classes. However, in the preliminary experiments, we find that laking an. from a random class (e.g., among 200 bird classes) produces "easy" negative pairs (z, nn ), i.e. images are often too visually different, not strongly encouraging S learn focus on subtle differences between fino-grained species as effectively as our "hard" negatives sampling.
Sampling using classifier C First, observe that pretrained classifiers C often have very high top-10 accuracy (e.g,, 98.63% CUB-200; Appendix G) and therefore tend to place species visually similar to the ground-truth class among the top-Q labels. Therefore, we leverage the predictions C(x) of the classifier C on x to sample hard negatives. That is, we sample Q nn. nearest images to the query. Yet, each nn. is from a class among the top-Q predicted labels for x, i.e., from the C(x) (see example pairs in Appendix [.3] As illustrated in Fig. | if the groundtruth class appears in the top-Q labels, we exclude that corresponding negative pair, arriving at Q- negative pairs. In this case, we will remove one positive pair make the data balance. In sum, if the groundtruth class is in the top-Q labels, will produce Q positive and Q negative pairs. Otherwise, we would produce Q posilive and Q negative pairs.
Empirically, we try Q {3,5, 10, and find Q 10 to yield the best comparator based on its test-set binary-cassification accuracy (sco the results of tuning Q in Appendix B.2 See Appendix for further experiments supporting our design choices.
4 Results
In this section, we demonstrate that PCNN examples enhance both Al and human accuracy. First, we use PCNN examples to train an image comparator S, which improves classifier C's predictions via the ro-ranking algorithm described in Sec. 3.3, Second, when shown PCNN examples, human users increase their accuracy in distinguishing correct from incorrect predictions by nearly +10 points.

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4.1 C S re-ranking consistently outperforms classifier C
Here, we aim to test how re-ranking algorithm (Sec. 3.3 improves upon the original classifiers C.
Experiment For cach of the 10 classifiers listed in Sec. 3. 2
we train corresponding comparator S
(following the procedure described in Sec. 3.4 and form a C x S model.
Table 1: On all three ResNet (RN) architectures and three datasets, our Cx consistently improves the top-1 classification accuracy (%) over the original classifiers C (e.g., by +11.48 on CUB-200) and also a baseline re-ranking C S (which uses only S scores in re-ranking). "Pretraining" column specifies the datasets that C models were pretrained (before fine-tuning on the target dataset).
Classifier architecture
Nes
18 (a)
NesNet- 34 (b)
Res Net-50 (c)
Dataset
Protraining
C
C
S
Cx S
C
C
8
C S
C
C
S
C S
CUB-200
NNtturalist
n/a
n/a
n/a
n/a
n/a
85.83
87.72
88.59 (+2.76)
Net
60.22
66.78
71.09 (+10.87)
62.81
71.92
74.59
(+11.78)
62.98
71.63
74.46 (+11.48)
Cars- 196
Net
86.17
85.70
88.27 (+2.10)
82.99
83.57
86,02
(+3.03)
89.73
89.90
91.06 (+1.33)
Dogs-120 120
ImageNNt
78.75
75.34
79.58 (+0.83)
82.58
80.82
83.62
(+1.04
85.82
83.39
86.31 (+0.49)
Results Our C x S models outperform classifiers C consistently across all three architectures (ResNet-18,
ResNet-34, and ResNet-50) and all three datasets (see Tab. D The largest gains on CUB-200, Cars-196, and Dogs-120 are +11.78, +3.03, and +1.04 percentage points (pp), respectively. That is, our method works best on bird images, followed by cars and dogs.
A trend (see Tab. D and Fig. 6) is that when the original classifier C is weaker, our re-ranking often yields a larger gain. Intuitively, weaker classifier's predictions benefit more from revising based on extra evidence (PCNN) and external model (comparator S). Yet, on CUB-200, we also improve upon the best model (iNaturalist- prreraiiid- ResNet-50) by +2.76 (85.83% + 88.59%) Note that while the gains on Dogs-120 are modest (Tab. D dog images are the noisiest among the three tested image types, and therefore the small but consistent gains on Dogs-121 120 are encouraging.
See Fig. 5 for examples of how our C x S model corrects originally wrong predictions made by ResNet-50 (from Indigo Bunting Green Jay on CUB-200, from Aston Martin Ferrari FF on Cars-196, and from Irish Terrier Otterhound). More qualitative examples are in Appendix L.
4.2 Hyperparameter tuning and ablation studies
We perform thorough tests over many choices for each hyperparameter and present the main findings below.
At test time, comparing input with multiple nearest neighbors yields slightly better weights for re-ranking Our proposed re-ranking (Fig. 2 takes n = image per class among the top- K classes to compute similarity score indicating how well the input matches class. However, would one nearest image be sufficient to represent an entire class?
We find that for each class among the top-K, increasing from n= 1 to 2 or 3 then taking the average similarity scores over the n image pairs only marginally increase the C x S accuracy (from 88.59% + 88.83% on CUB-200 and 91.09% + 91.20% on Cars-196; full results in Appendix B.I.. h. However, these marginal gains require querying S two to three times more, entailing a much slower run time. Therefore, we propose to use n 1 nearest neighbor per class.
Sampling image pairs from the top-Q classes where Q= 10 A key to training our comparator (Fig, is to sample image pairs from the top-Q labels predicted by C. We test training S using different values of Q = 3, 5, 10, 15 and find that the comparator's accuracy starts to saturate at Q = 10 (Appendix B.2 Hence, we use Q = 10 in all experiments.
Hard negatives are more useful than easy, random negatives in training S We find that training S using negative pairs constructed using the nearest nn. from random classes among the 200 CUB classes yields a RN50 x S accuracy of 86.5%%. In contrast, if we sample negative pairs from the top-Q predicted classes returned by C, the accuracy substantially increases by +2.04 pp 88.59% (as reported in Tab. 2
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In another experiment, repeat our hard-negative sampling from the top-Q classes but in each class, we take the 2nd or 3rd image (instead of the 1st.) nearest. to the query image to be the class-representativ image. Yet, the C x S accuracy decreases from 88.59% to 88.06% and 88.21%, respectively. In sum, both experiments show that sampling negative pairs using the 1st nearest neighbors from probable classes yields substantially better comparators than from random classes (Appendix B.4
Re-ranking using the x S product is better than using the scores of alone To understand the importance of the product of two scores C(x) S(x, nn) in re-ranking (Fig. 2 test ablating C(+) away from this product, using only nn), scores to re-rank the lop-predicted labels. We refer to this approach as C S
We find C ~ re-ranking to sometimes even hurt the accuracy compared to the original C (e.g., 78.75% 75.34% on Dogs-120; Tab. D Yet, with the same comparator S, using the product of two scores x S consistently outperforms not only the original classifier C but also this C bascline re-ranking by an average gain of +3.27, +2.06, and +3.32 pp on CUB-200, Cars-196, and Dogs-120, respectively (Tab.
Augment improves generalization of S Over all three datasets, when training comparators using only training-sct images (no augmentation), the show that the models overfit.
loss curves (full results in Appendix B.8
To combat this issue, besides early stopping, we incorporate TrivialAugment Müller & Hutter 2021 into the training and find help the learning to converge faster and reach better accuracy. Compared
Initial class ranking by pretrained classifier
Query: Green Jay
Topl: Indigo Bunting
Top2: Greenjay
Top3: Blue Jay
Top4: Cape Glossy Starling
Top5: Painted Bunting
5: Refined class ranking by Product of Experts
10%|
Topl: Green Jay
Tap2: Indigo Bunting
Top3: Painted Bunting
Tap4: Cape Glossy Starling
Top5: Blue Jay
(a) CUB- 200 re-ranking:
Indigo Bunting
Green Jay.
clasdfien
Inttal
closifier
(b) 196 re-ranking: BMW
Jaguar XK.
(c) Dogs- 120 re-ranking: Irish Terrier Otterhound.
Figure 5: C S model successfully corrects originally wrong predictions made by ResNet-50.

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using no data augmentation, TrivialAugment leads to test-set binary-classffiatiion accuracy gains of +1.32 on CUB-200, +1.52 on Cars-196, and +1.21 on Dogs-120.
4.3 Training comparators on image pairs is key
At the core of our method is the image comparator S. Yet, one might wonder: (1) Is training S necessary? Would using cosine similarity in common feature space be sufficient? and (2) How important are the components (self-attention, cross-attention, MLP) of the comparator?
Both cosine and Earth Mover's distance in a fine-grained image classifter's feature space poorly separate visually similar species. Contrastively training comparators is a key. First, we test using cosine similarity in the pretrained feature space of a classifier (aessri et al. (2022) of 85.83% accuracy CUB-200. That is, we use the aaggool features of ResNet-50 (i.e., average pooling after the last conv layer) to be our image embeddings. However, we find that C - S ro-ranking using this similarity metric leads to poor CUB-200 accuracy compared to using our trained comparator (60.20% vs. 87.72%).
We also test re-ranking using patch-wise similarity by following Phan & Nguyen (0022 Zhang et al. (2020) and using the Earth Mover's distance (EMD) at the 49-patch embodding space (7 x 7 x 2048) of [laye4 Yet, this patch-wise image similarity also leads to poor accuracy (54.83)). In sum, we find the pretrained conv features of a state-of-the-ars classifier are not sufficiently discriminative to separate probable classes (Fig. 2
In the next experiment, we find that training even a simple MLP contrastively on the image pairs sampled following our C-based sampling (Sec. 3.4.3 dramatically improves the re-ranking to 83.76% on CUB-200.
MLP in the comparator S plays the major role and adding Transformer blocks slightly improves accuracy further We perform an ablation study to understand the importance of the four main components- finetuned features f, MHSA, cross attention, and ML.P- of the proposed comparator architecture (Fig. 3
First, we find that finetuning f while training the comparator leads to better binary classification accuracy (94.44% vs. 91.94%; see Appendix|B.r Second, we find that training the 4-layer MLP with the pretrained features f (i.e., -=0, no MSHA and no cross attention) leads to comparator that has C + S re-ranking accuracy of 83.76% on CUB-200. Adding MSHA and cross-attention blocks led to the best comparator, pushing this number by +3.55 to 87.31%. More details are in Appendix B.9
Sanity checks We perform additional sanity checks to confirm our trained binary classifier works as expected. Indeed, S outputs Same class 100% of the time when two input images are identical and nearly 0% of the time given a pair of two random images (that are likely from two different classes) (Appendix D. Another insight is that the comparator tends to assign higher mean confidence scores when it is correct compared to when it is wrong (Appendix F.2).
4.4 Well-trained comparator S works well with an arbitrary classifier C in a C x S model
Sec. 4.1 shows that using given comparator S, trained on data sampled based specific classifier C₁, one can form a C₁ S1 model that outperforms the original C1.
Here, we test if such coupling is necessary: Would the same comparator S₁ form a high-performing C x S with an unseen, black-box classifier C2?
First, we take an image comparator S, from Sec. 4.1 that was trained on data sampled based on a high-performing CUB ResNet-50 classifier by Tacsiri et al. (2022) (85.8%% accuracy: Tab. 1:) Then, in the C x S setup (Sec. 3.3) ; we test using the same S, to re-rank the predictions of five different CUB classifiers: three different ResNets (Tab. D ViT/B-16 (82.40%). and an NTS-Net (87.04%) Yang et al. 2018).
Experiment
Results Interestingly, S works well with many unseen, black-box classifiers in a C x S model, consistently increasing the accuracy over the classifiers alone. Across unseen classifiers, we witness significant accuracy boosts (Fig. 6 --) of around +20 pp for all ResNet models, which were pretrained on ImageNet and finctuned on CUB-200. With ViT and NTS-Net, the gains are smaller, yet consistent. Detailed quantitative numbers are reported in Appendix
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INIK-N118
CUB-200 Top-1 Accuracy (%)
IN1K-RN34
IN1K-RN50
IN1KVIT-B-16
80
INat-RN50
NTS-Net
75
Unseen
60
12M
20M
30M
100M
120M
180M
Number of Parameters
Figure 6: When pairing well-trained comparator S₁ with an unseen, black-box classifier our C x S models (*) consistently yields a higher accuracy on CUB-200. Along the x-axis, each shows the total number of parameters of S and a paired classifier.
4.5
C outperforms prototype-based classifiers
Our PCNN-based approach is related many explainable methods that compares the input to examplar images Papernot & McDaniel (0018), prototypical patches Chen et al. (2019) or both images and patches Thesiri et al. 2022 Here, aim to compare our method with these state-of-the-art models on CUB-200 Tab. 20. Cars- (Tab. and Dogs-120 (Tab.
k-Ncarest Neighbors (k-NN) operates by comparing an input image against the entire training set at the image level. In contrast, our S uses only images from 10 classes out of all classes (e.g. 200 for CUB) for re-ranking. Following Taesiri al. (2022). we use k 20 and implement a bascline (k-NN that compares images using cosine similarity in the pretrained ResNet-50's avgpool features (Tab. 85.46% accuracy). To understand the importance of our re-ranking, also test another k-NN bascline that comparator's scores instead of cosine similarily.
Consistent results across all three datasets First, we find that using S as the distance function for k-NN results competitive accuracy on all three datasets, specifically, 86.88% on CUB-200 (Tab. 2 and 88.90% on Cars-199 (Tab. 3). This k-NN S result shows that is strong generic image comparator. That is, here, S can be used to compare training-set images that are even outside the distribution of top-Q classes that it was trained
Second, our C x S outperforms both A-NN + cosine and A-NN S baselines consistently by around +1 to +3 pp. suggesting that the S-based re-ranking method is effective.
Third, our C method outperforms the state-of-the-ar explainable re-ranking methods of CHM-Corr and EMD-Corr Taesiri et al. 2022 by a large margin of around +4 on CUR-200 and Cars-196 (Tabs. 2 and 3) and +0.73 on Dogs-120 (Tab. While both techniques are 2-stage classification, the major technical difference is that our C leverages high-performing black-box C and further finetune its top-K K predicted labels using PCNNs. In contrast, CHM-Corr and EMD- -orr relies N-NN to first rank the training-set images, and then re-rank the shortlisted images in their 2nd stage.
slow and often lower-perfomiigg
Fourth, by a large margin, our x S outperforms all models in the prolotypical-part-bess learn representative patch prototypes for each class and compare them to the input image at test time, From the explainability viewpoint, C reveals insights into how labels are re-ranked but how they are initially predicted by the black-box C. In contrast, prototype-basee family aims to show insights into how the similarity between imput patches and prototypes contributes initial image-label predictions.
family, which
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Table 2: CUB-200 top-1 test-set accuracy (%). All models are finetuned on CUB-200 from an iNaturalist- pretrained ResNet-50 backbone. For our method, we report the mean and std over random seeds (sce Sec. B.7.. Prototypical part-based classifiers typically use full, uncropped images, and 10 part prototypes per class. Accuracy is from Wang al. (2023b).
E: Using training-set Examples at lest time to make predictions.
I: Comparing images at the Image level.
P: Comparing images at the Patch level.
R: Re-ranking initial prodictions of a classifier.
Classifier
Ex
Ing
Patch
R
Acc
k-NN cosine Taesiri al.
2022
85.46
k-NN + S
V
86.88
ProtoPNet Chen et al.
20199
81.10*
PIPNet Nanta al. 2023
82.00
ProtoTree Naula el, a.
2021
82.20
ProtoPool Rymarczyk
2022
85.50
Def-Proto Donnelly et al.
2021
86.40
TesNet Wang al. [2021]
86.501
ST-ProtoPNet Wang et al.
(2023b)
86.60
ProtoKNN
Ukai et al. [2023)
87.00
CHM-Corr
Taesiri
al.
2022
83.27
EMD-CorT
Taesiri et al.
2022
84.98
88.59
C S (ours)
0.17
Table 3: Cars-196 top-1 lest-set accuracy (%). All models are finetuned on Cars- 196 from an ImageNet- pretrained ResNet-50 backbone. Accuracy of Pro- toPNet is from Keswani et al. (2022) & accuracy" of
Table 4: Dogs-120 top-1 test-set accuracy (%). All models are finetuned Dogs-120 from an ImageNet- pretrained ResNet-50 backbone. Accuracyf is from
ProtoPShare Rymarczyk et al. (2211) is from Nauta
Wang et al. (023b) and MGProto is from Wang et al. (2023a)
et al.
2023).
Classifier
Ex
Img
Patch
R
Acc
Classifier
Ex
Img
Patch
R
Ace
k-NN + cosine
87.48
k-NN + cosine
85.56
k-NN + S
/
/
88.90
k-NN + S
/
/
82.33
ProtoPNet
/
85.31
ProtoPNet
/
76.40'
ProtoPShare
/
86.40"
TesNet
82.40
PIPNet
86.50
Def-ProtoPNet
82.20
ProtoTrre
/
86.60
ST-ProtoPNet
84.00
ProtoPool
7
88.90
MGProto
85.40
ProtoKNN
/
90.20
CHM-Corr
/
/
85.59
CHM-Corr
85.03
EMD-CorF
/
85.57
EMD-Corr
/
87.40
86.31
C x S (ours)
91.06
* 0.03
S (ours)
0.15
4.6 PCNN improves human accuracy in predicting Al misclassifications on bird images
Given the effectiveness of PCNN examples in re-ranking,
motivated to test them human users.
Specifically, we compare user' accuracy in the distinction task Kim et al. (2022) (i.e., telling whether a given classifier C is correct or not) when presented with top-1 class examples as in prior work Nguyen et al. 2021 Taesiri et al. (2022) compared to when presented with PCNN (Fig. vs. b).
Experiment We randomly sample 300 correctly classified and 300 misclassified images by the C S model from the CUB-200 test set (88.59% accuracy; Tab. 2 From our institution, we recruit 33 lay users for the test with top-1 class examples and 27 users for the PCNN test. Per test, each participant is given 30
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Input
Coplen Tam
Tem
Crodee Dats
Cocten Ten
Coptan Tem
(a) top-1
YES
Iiput
Oistinn tmn
Nojrt
CETRESS
c3 o
#xmann
(b) PONN
YES
NO
Figure both experiments, humans are asked whether the input image is Caspian Ter given that input, model prediction, and either top-1 class examples (top) PCNN explanations (bottom). When given only examples from the top-1 class, humans tend to accept the prediction, not knowing there are other very visually similar birds. Yet, the top-5 classes provide humans with broader context which leads to better accuracy (64.58% vs. 54.55%; Sec. 4.6
images, one at time, and asked predict (Yes or No) whether the top-1 predicted label is correct given the explanations (Fig. 7 To align with prior work, we choose K = when implementing PCNN, i.e. we only show nearest examples from top-K (where K = 5) classes to keep the explanations readable to users. More details in Appendix
Results We find that PCNN offers contrastive evidence for users to distinguish closely similar species, leading to better accuracy. That is, showing only examples from the top-1 class leads users to overly trust model predictions, rejecting only 22.28% of the cases where the AT misclassífies. In contrast, PCNN users correctly reject 49.31% of Al's misclassifications (Fig. Bp). Because users are given more contrastive evidence to make decisions, they lend to doubt Al decisions more oflen, resulting in lower accuracy when the AI is actually correct (79.78% vs. 90.99%; Fig. 8a).
Yet, on average, compared to showing only examples from the top-1 class in prior works Nguyen et al. 2021
Taesiri et al. 2022 Nguyen et al. (2024), PCNN improves user accuracy by a large margin of nearly +10 pp (54.55% 64.58%). Our finding aligns with the literature that showing explanations helps users reduce
over-reliance on machine predictions Buçinca et al. (2021): Schemmer
(2023); Chen et al. (2023a). We
present extensive details of the human study in Tab. 17
5 Limitations
While re-ranking method shows novel and exciting use of nearest neighbors, it comes with major cime-compyyxity limitation. That is, the re-ranking inherently adds extra inference time since also need to query the comparator S K times in addition to only calling the original classifier C. Empirically, when C is ResNet-50 and K = 10, our C Our method runs also slower than k-NN and prototypical part-based classifiers Chen et al. (2019) but faster than CHM-Corr and EMD-Corr re-rankers Tacsiri et al. (2022). For a detailed runtime comparison between other existing explainable classifiers and our method, along with our proposals to reduce runtime without sacrifying the classification accuracy, refer to Appendix F
model is ~7.3x slower (64.55 seconds vs. 8.81 seconds for 1,000 queries).
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Al is Correct
is Wrong
Per-user Mean Accuracy (%)
80
40
90.99
79.78
22.28
49.31
top-1
PCNN
top-1
PCNN
Type NN
Figure 8: Users often accept when top-1 class neighbors presented, leading to high accuracy when the AI is correct (a) and extremely poor accuracy when Al is wrong (b). PCNN ameliorates this limitation of the top-1 examples.
6 Discussion and Conclusion
In this work, we propose PCNN, a novel explanation consisting of K images laken from the top-K predicted classes to more fully represent the query. We first show that PCNN can be leveraged improve the accuracy of fine-grained image classification system without having to re-train the black-box classifier C, which is increasingly common scenario in this foundation model era. Second, we show that such well-trained comparator S could be used with any arbitrary classifier C. Our method also achieves state-of-the-art classification accuracy compared to existing prototype-based classifiers on CUB-200, Cars-196, and Dogs- 120. Lastly, we find that showing PCNN also helps humans improve their decision-making accuracy compared to showing only top-1 class examples, which is a common practice in the literature.
An important aspect to consider that while PCNN provides comprchensive explanation by presenting multiple probable-class examples, is crucial to ensure this does not reduce user confidence to degree that negatively affects preision-making especially in time-critical high-stakes scenarios. Balancing detailed information with user confidence is highly important for effective human-AI collaboration.
Acknowledgement
We thank Son Nguyen and Hung Dao from KAIST, South Korea and Peijie Chen, Thang Pham, and Pooyan Rahmanzadehgervi from Auburn University for feedback on carly results. AN was supported by the NSF Grant No. 1850117 & 2145767, and donations from NaphCare Foundation & Adobe Research. GN was supported by Auburn University Presidential Graduate Research Fellowship.
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A Additional details of training, evaluation, and architecture
Unless otherwise stated, the same sced value of 42 set to ensure reproducibility across Python, Numpy, and PyTTrrc operations. We employ the faiss (Johnson et al. 2019) library to index and retrieve nearest. neighbors, utilizing the L2 distance through the IndexFlatL2 function.
A.1 Training
Our experiments were conducted using two NVIDIA A100-SXM4440GB GPUs, leveraging PyTorch version 1.13.0+cul17.
CUB-200 From the training set of 5,994 images of CUB-2000, follow the sampling procedure in Fig. and collect a total of 113,886 pairs (where the positive pairs and negative pairs take e 50%).
Cars-196 We repeat the same procedure but collect 154,736 pairs with the same positive/negative ratio from 8,144 images.
Dogs-120 120 We collect 228,000 pairs with the same positive/negative ratio from 12,000 dog samples.
In training image comparator S, given B image pairs training samples, and true binary label y (positive negative) for each i-th sample pair, train S to minimize the standard binary cross-entropy loss:
B Σ y log (sigmoid(o:)) + (1 - y:) log (1 sigmoid(o:))
L =
(1)
B ml
To select the best training checkpoint for our image comparator S during training, we use the F1 score, which balances precision and recall, as our key metrie. This process involves evaluating the model al each of the 100 training epochs. The F1 score helps us identify the checkpoint where the model best distinguishes between similar (positive) and dissimilar (negative) image pairs, ensuring a balance between accurately identifying true positives and minimizing false positives. This method aids in choosing model that is not only accurate but also generalizes well, avoiding overfitting.
For CUB-200, train S for 100 epochs with a batch size of 256 and a max learning rate of 0.01 for OneCycleLR Smith & Topin (0199) scheduler. The model architecture for this dataset is defined by M N 4 4 and L = 2. For Cars-196 and Dogs-120, we use the same batch size and number of epochs, but adjust the max learning rate to 0.1 and 0.01 for OneCycleLR schoduler, respectively. both datasets, we use M N 3 and 3. Unless specified differently, both the self- and cross-atlention Transformer blocks have 8 heads, and for the sampling process, Q 10.
For the pretrained convolutional layers f, always use the last conv layer in ResNet architectures. f is the layer4 per PyyTorh definition or conv5 in He et al. (2016). The conv features given by f are Xconvl and Xconv2 E (220037777) for ResNet-50 and 1512x7*1) for both ResNet-34 and ResNet-18.
The MLPs consist layers, with each utilizing Gaussian Error Linear Units (GeLU) Hendrycks Gimpel (2016) and Batch Normalization Toffe Szegedy 20155. except for the last two layers. See the definition of
MLPs below:
MLP architecture
self net Sequential mn. Linear (onnear(inut..... (input_ding dim, 512), mn Bm. nn. GG.LEU()
nn. Linean (512, hidden dim). nn. -BatchNormld (hidddrerddddddennnn.a hhiden_diim nn. (ELU(),
mn. Linear (hidden_dii dim, 2).
Linear (2, 1))
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In our neural network configuration, the variable hidden_dind dim empirically set to 32. The variable input_ _dim, however, varies depending the specific ResNet architecture employed. Specifically, for ResNet-50, input_ dim is set to 4098, whereas for both ResNet-34 and ResNet-18, input_dd _dimm is set to 1026.
A.2 Evaluation
evaluate the performance of the comparator S on the same task as during training, we employ the sampling procedure outlined Fig. on the test dataset.
For example, within the test set of CUB-200, which consists of 5,794 samples, we generate 110,086 pairs. Although we have already excluded the first nearest neighbor from the ground-truth class to avoid z and nn being similar, we still identify 740 pairs among these that consist of identical images, and then we remove them, resulting in total of 109,346 pairs. This set includes 49,750 positive pairs and 59,596 negative pairs. In an effort to balance the distribution between positive and negative pairs, we eliminate an additional 9,846 negative pairs. This adjustment ensures that we are left with 99,500 pairs, achieving an exact 50/50 /0 split. between positive and negative pairs.
Following the same procedure 8041 Cars-196 test samples, start with 152,779 pairs and remove 6,945 negative pairs, resulting in balanced total of 145,834 pairs, with exact 50/50 split between positives and negatives.
For Dogs-120, starting with 8,580 samples, we apply the same procedure to generate 163,020 pairs. To achieve balance, we eliminate 13,414 negative pairs. This results a total 149,606 pairs, ensuring a 50/50 split between positive and negative pairs.
The output score produced by the comparator S is then subjected to threshold of 0.5 to classify each pair either positive negative.
A.3 Architecture
Referring to Fig. 3 the written description of the architecture of the image comparator S are:
Xconv1 f (x);
Xcunv2 f (Xnn)
Xconv1 flatten (Xconv1) )" :econv2 flatten (Xcouvz)
X1
] Xposi
X2 = [Xcle Xcouve ] } Xpos
Y1 X + MHSA(x));; Y2 = X2 MHSA(X))
(2)
z1, z2 CrossAttn (y1,y2)
MLPs (concat (z, [0,:], z2 (.::)))
sigmoid (o)
B Hyperparameter tuning and ablation studies
In the following experiments, unless otherwise stated, we use cesturalist-reetraime ResNet-50 Tacsiri al. (2022) for CUB-200 and ImageNet-prretaiine ResNet-50 for Cars-196 and Dogs-120. Also, evaluate design choices using binary classification accuracy (%) for the comparator S and top-1 classification accuracy for the re-ranking algorithm.
B.1 At test time, comparing input with multiple nearest neighbors achieves better weights for re-ranking
Considered datasets: CUB-200 and Cars-199 196. Considered tasks: 200/ 200/196-way 196-way image classification.
In our re-ranking algorithm (Fig. we only use one nearest neighbor (1 un) compare with the input image to compute the similarity score for given class. Tn this section, aim to study whether presenting
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more nearest-ncighbor (NN) samples to the comparator provides better blasswiss re-ranking weights for the algorithm.
For each class, we perform the comparison between and nn 2 or 3 times (i.e., pairing the input image 2 with each of the 2 or 3 nearest neighbors from each class) and then take the average of these scores to determine the final weights. We then examine whether increasing the number of comparisons (by using more nearest neighbors) enhances the accuracy of the re-ranking process.
Table 5: Top-1 classification accuracy (%) using diffrent numbers nearest neighbors. Using more nearest. neighbors to compare with the input image slightly improves C S
Number of nearest neighbors CUB-200 Top-1 Acc (%)
Cars-199 Top-1 Acc (%)
88.59
91.06
2
88.75
91. 11.20
3
88.83
90.09
In Tab. 5 we find that incrcasing the number of nearest neighbors for comparison slightly improves the top-1 classification accuracy on both CUB-200 and Cars-196 With nearest neighbors, the accuracy on the test. set increases from 88.59% + 75% for CUB-200 and 91.06% + 91.20% for Cars-196. When using nearest neighbors, the accuracy increases to 88. 83% for CUB-200 and 90.09% for Cars-196. However, noteworthy consider the associated computational cost, as it increases linearly with the number of nearest neighbors. As such, careful balance between performance and computational efficiency should be struck.
B.2 Using Q = in sampling yields the optimal balance between the accuracy of and the training cost
Considered datasets: CUB-200. Considered tasks:
Binary classification.
Q is the crucial hyperparameter for training S (see Sec. 3.4.3 To assess their effects beyond the default
setting of Q 10, we also train S with configurations of Q = 3, 5, and 15. This value represents the coverage
of the sampling process over the nearest neighbor space.
Table 6: Binary classification accuracy (%) of S trained with different values of Q for CUB-200. This value has considerable influence on the final performance of S. Using fewer than 10 significantly diminishes performance, while increasing to 15 offers only a marginal improvement at the expense of higher training costs. The training time complexity increases linearly with the value of Q, following the formula 2Q - (Fig
Q
Train Binary Acc (%)
Test Binary Acc (%)
97.17
87.49
97.60
92.94
10
98.69
94.44
15
99.30
94.62
However, large values of Q could introduce noise during training. This is because NNs from the tails of the top-1 class (the last images in the middle row of Fig. 30a] and predicted classes (the last images in the bottom row of Fig. 330a significantly diverge from the head samples used in comparison with the query image during test time.
From Tab. 0 we notice that these values significantly influence the performance of S. Interestingly, there appears to be trend: as these values increase (indicating more training data), the test accuracy also goes up (scemingly saturated after 10). However, similar to Sec. B.1 there is balance to be considered between test accuracy and the computational cost.
B.3 Using K 10 in re-ranking yields the optimal classification accuracy
Considered datasets: CUB-200 and Dogs-120. Considered tasks: 100/120-way image classification.
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Given the optimal value of Q being 10 for training image comparators S demonstrated in Appendix B.2
tested various K values for the re-ranking algorithm described in Sec. 3.3 Empirically, we found that K 10 delivers the best classification accuracy on both CUB-200 and Dogs-120 datasets. Hence, we use K = 10 throughout our main experiments in the main text.
Table 7: Performance and runtime comparison for different values of K on CUB-200 and Dogs- 120 datasets.
K
CUB-200 Top-1 Acc (%)
Runtime (s)
Dogs- 120 Top-1 Acc (%)
Runtime (s)
85.83
8.81
85.82
8.81
87.95
(+2.12)
27.72
86.06 (+0.24)
50.35
3
88.28
(+2.45)
32.32
86.03 (+0.21)
54.96
5
88.28
(+2.45)
41.53
85.91 (+0.09)
64.
10
88.42
(+ (.59)
64.55
86,27 (+0.445
87.18
15
88.00
(+2.17)
87.57
85.86 (+0.04)
110.20
B.4 Using C's predicted labels to sample hard negative pairs is more beneficial than using random negative pairs
Considered datascts: CUB-200. Considered tasks: Binary classification 200-way image classification.
We run two experiments. First, we replace our proposed negative samples (from top-2 to top-Q class; see Fig. 2a) by random samples.
In our data sampling algorithm (Fig. the positive and negative classes are determined by classifier C and it is intriguing to study whether we can avoid the reliance on C to choose the classes for nearest neighbor retrievals. Therefore, we modify the data sampling algorithm in Sec. 3.4 so that for a training image v, the NNs for positives are from the ground-truth class and the NNs for negatives are randomly picked from the remaining class. We train an image comparator for CUB-200 with the same settings used for the one in Tab.
In this setup, negative pairs become much easier to classify due their stark visual dissimilarity, leading S to predominantly learn from positive pairs. Indeed, S demonstrates limited ability to identify the misclassifications made by the ResNet-50. When this image comparator S is utilized in the re-ranking algorithm (Fig, 2 for 200-way image classification, there is a notable decline in the top-1 accuracy from 88.59% - 86.56 55. This result. confirms the novelty of our sampling process and proves that hard negatives (close-species pairs in Sec. 3.4) are the key factor to help achieve competitive performance. Please note that in this experiment, the input sample still compared with its first nearest neighbors in test.
Second, we the 2nd or 3rd closest nearest neighbors (NNs) in each class instead of using the 1st to train S. We find that using the 2nd and 3rd NNs in the sampling process (both in training and test) is suboptimal, decreasing the top-1 accuracy from 88.59% 88.06% and 88.21%, respectively.
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B.5 Simple and parametric similarity functions are insufficient to distinguish fine- line-grained species
Considered datasets: CUB-200. Considered tasks: 200-way image classification.
To understand the significance of image comparator S for re-ranking, we consider three alternatives in Tab. S (a) cosine similarity non-parametric, imago-lovel, (b) Earth Mover's Distance (EMD): non-parametric, patch-level, and (e) 4-layer MLP (Appendix []]: parametric. We use avgpool features for cosine and layer4 cony features for EMD and the 4-layer MLP. All these three functions fully manage the re-ranking, akin the role of S in C S.
(a) We replace with cosine similarity function, which chooses the class that yields the highest similarity to the input, among top-Q categories ranked by ResNet-50, as the final prediction. However, we find this degrades the top-1 accuracy from 87.72% + 60.20%. (b) We use EMD function from Zhang et al. (2020) to also pick the closest class to the input but observe a drop from 87.72% - 54.83%. (c) We remove Transformer layers in Fig. and train comparator network (with f being frozen) following the same settings used for S in Sec. 3 Yet, this simple comparator leads to reduced accuracy of 83.76%.
Table 8: Top-1 classification accuracy on CUB-200 when we use 2nd or 3rd NN samples for negative pairs instead of the 1st NNs as depicted in Fig. For our re-ranking algorithm, 1st is optimal. We also experiment with different similarity functions for re-ranking and find that comparator S the best.
NN-th
RN50
+
re-ranking on CUB-2 200 (%)
Our trained
cosine
EMD
4-layer MLP
1st
88.59
87.72
60.20
54.83
83.76
2nd
88.06
87.34
58.84
57.05
84.33
3rd
88.21
87.43
57.47
57.14
83.93
Below we show examples where cosine cannot assign distinctively high similarity scores for groundtruth classes among other top-probable classes, thus being unable to re-rank RN50 predictions effectively.
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Reranking using comparator scores
Query: American Crow
Top1: Shiny Cowbird
Top2: Fish Crow
Top American Crow
Top4: Brewer Blackbird
Top5: Common Reven
S:
Reranking using cosine similarity
Top1: Shiny Cowbird
Top2: Rish Crow
Top3: American Crow
Top4: Brewer Blackbird
Top5: Common Raven
Figure 9: A pretrained RN50 model predicts the Query image's class (ground-truth label: American Crow) and produces initial ranking, but the top-1 predicted class (Shiny Cowbird: 88%) does not match the ground-truth. Re-ranking using scores from the comparator alone correctly identifies the top-1 class as American Crow, whereas cosine similarity scores fail to do so.
Reranking using comparator scores
Query: Kentucky Warbler
Top1: Hooded Wartior
Warbler
Warbler
Conada Yellawthreat
S:
RNSO: 5:
1% S:
0% S:
0% S
Reranking using cosine similarity scores
Top1: Hooded Warbler
Top2: Kentucky Warbler
Tap3: Wilssnn Warbler
Top4: Canada Warbler
Top5: Common Yellowthroat
Figure 10: A pretrained RN50 model predicts the Query image's class (ground-truth label: Kentucky Warbler) and produces initial ranking, but the top-1 predicted class (Hooded Warbler: 88%) does not match the ground-truth. Re-ranking using scores from the comparator S alone correctly identifies the top-1 class Kentucky Warbler, whereas cosine similarity scores fail to do so.
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B.6 Finetuning pretrained conv features is important in training S
Considered datasets: CUR-200. Considered tasks:
Binary classification.
In the training process of S, one crucial consideration is whether to optimize the pretrained conv layers for the new task. If these layers are already generating effective encodings for the binary classification task, then should focus optimizing the remaining layers. To evaluate the effects of retraining the convolutional (conv) layers, we train an image comparator S for CUB-200, ensuring that the conv layers remained frozen while all other settings were maintained as they were specified in Sec. A
We find that freezing the cony layers significantly slows down the learning process S. Specifically, the model could not converge after 100 epochs (training accuracy of 94.18%) and the test accuracy of 91.94% (lower than 94.44% when making conv layers trainable in Tab.
B.7 The performance of S and re-ranking algorithm is statistically consistent
Considered datasets: CUB-200, Cars-196, and Dogs- Considered tasks: Binary classification &
200/196/120-ae image classification.
To ensure the statistical significance of S's performance, we train it multiple times on CUB-200 and Cars-196 using various random seeds and present the results in Tab.
Table 9: Binary classification accuracy of S (%) and top-1 classification accuracy (%) of Product of Experts
C x S over 3 random seeds. The performance of S and our re-raking algorithm C x S is statistically consistent.
Train Binary Ace (%)
Test Binary Acc (%)
C x S Top-1 Acc (%)
#
CUB-200
Cars-
Dogs-120
120
CUB-200
Cars- 196
Dogs- 120
CUB-200
Cars- 196
Dogs-120
1
98.69
99.01
98.83
94.37
94.97
92.01
88.42
90.86
86.27
2
98.66
98.99
98.79
94.53
95.07
92.09
88.82
91.23
86.33
3
98.61
99.02
98.81
94.44
95.04
92.96
88.52
91.08
86.32
98.
08.0040.04
99.00+0.02
98.81+0.02
94.44+0.06
95.02+0.04
92.05=0.03
88.59+0.17
91.06=0.15
86.31=0.02
We see that S consistently achieves similar test accuracies for both binary and single-label classification tasks across different runs. Further, we note that misclassifies pairs on CUB-200 and Cars- 196. We later find in Sec. 1.2 that these errors are primarily due to inter-class and intra-class variations, as well as issues with dataset annotations.
B.8 Removing data augmentation degrades the performance of S
Considered datasets: CUB-200, Cars-196 196, and Dogs- 120s-120. Considered tasks: Binary classification.
We wish to study the impact of data, augmentation experiments for CUB-200 and Cars-196 datasets without applying TrivialAugment (Müller & Hutter 2021 In Tab. I0 we observe that the model not only converges more quickly in training without angmentation but also attains higher accuracies over the training set.
the performance of S by repeating previous
Table 10: Binary classification accuracy (%) of S on CUB-200 and Cars-196 with and without Trivial Augmentation Müller & Hutter (2021) in training. Data augmentation from Trivial Augmentation plays important role in the performance of S.
CUB-20 200
Cars- 196
Dogs- 120
Phase
with
without
with
without
with
without
Train Binary Acc (%)
98.65
99.81
99.00
99.79
98.81
99.82
Test Binary A%c(%)
94.44
93.12
95.02
93.50
92.05
90.84
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Nonetheless, the omission of data augmentation has significantly negative effect during test time (i.e. hurting model robustness). S achieves 94.44% with data augmentation, while the omission results in a lower accuracy of 93% for CUB-200. Similarly on Cars-190 196 and Dogs- 1egs-120, we observe drops in test accuracy without using the data augmentation technique, from 95.02% - 93.50% and 92.05% - 90.84%. These findings underscore the importance of data augmentation in the training of the image comparator, as it helps combat. overfitting. This finding comes as no surprise, as the effectiveness of data augmentation in-the-wild datasets has been well-proven in literature Nguyen et al. (2023).
B.9 The importance of the finetuned feature f, self-attention, cross-attention, and MLP in comparator
Considered datasets: CUB-200. Considered tasks: 200-way image classification.
To study the effects of different image comparators S on re-ranking, we unfolds systematic exploration of architectural configuratious, revealing that our proposed archilecture in Fig. 3 is optimal. We allempl to re-rank the top-Q CUB-200 categories given by an iNaturalist- preeraiidd ResNet-50.
We begin with a simple bascline, where the comparator in C + is substituted by a cosine similarity function. However, this approach only yields an accuracy of 60.20%.
Next, while keeping the pretrained conv layers f still frozen, we train a comparator S consisting of the frozen f followed by 4-layer MLP defined in Sec. A.I to distinguish positive and negative pairs the task described in Sec. 3.4.2 Using C - S for re-ranking, see an increase from 60.20% 83.76%. Despite this improvement, the achieved accuracy does not surpass that of the standalone pretrained classifier C, which has an accuracy of 85.83%.
We then proceed to fine-tune the pretrained convolutional layers tandem with the 4-layer MLP. This fine-tuning process enables f to better adapt its learned reprosentations for the image comparison task. As a result, we observe an enhancement in accuracy from 83.76% 87.31%, marking an improvement over the pretrained ResNet-50 by +1.48 pp.
Finally, we integrate transformer layers into our model as depicted in Fig. 3 and we train S using training settings that are consistent with those previously described. This helps S further refine its performance, elevating the accuracy from 87.31% + 87.72.
C Well-trained comparator S works well with an arbitrary classifier C
Considered datasets: CUB-200. Considered tasks: 200-way image classification.
We have shown that using a comparator S trained with a classifier C improves the classifier's performance. Here we wish to investigate whether well-trained compartor S can be used with other arbitrary black- box, pretrained image classifiers to improve them through our re-ranking algorithms. We refer these classifiers "unseen models" to distinguish them from the classifter C that S has worked with during training.
Table 11: Top-1 classification accuracy (%) of classifiers seen and unseen during the training of S. When coupling black-box, unseen classifiers with S, we significant and consistent, accuracy boosts CUB-200.
Seen
Unseen
Model
iNat-RN50
INIK-RN18
IN1K-RN34
INIK-RN50
ViT-B-16
NTS-Net
C
85.83
60.22
62.81
62.98
82.40
87.04
C - S
87.73
84.38
84.74
84.52
87.66
87.94
C x S
88.42
83.60
83.83
83.62
88.45
87.14
In Tab. I we denote C as the pretrained classifiers, while C + S and C x S represent our proposed re-ranking algorithms, detailed further Scction [ The notation INIK refers to models pretrained on the ImageNet- 1K dataset Deng et al. (2009). Using an image comparator S, well-trained with the iNaturalist- pretrained ResNet-50 (NNat-RN50) observe significant improvements in the accuracy of classifiers not.
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seen during training. Specifically, improvements are up to 23.28 pp for C x S and 24.16 pp for C S when applied to INIK-RN18.
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D Re-ranking algorithms for single-label image classification
D.1 Baseline classifiers
ResNet: In Sec. 3.3 we introduced re-ranking algorithms that reorder the top-predicted classes of a pretrained classifier, denoted as C. Given a pretrained ResNet, we wish to investigate whether our classifier can improve over the classifier C working alone.
k-Nearest Neighbors (kNN): kNN classifiers operate by comparing imput image against the whole training dataset at the image level (image-to-image comparison). Our classifier considers only a small, selective subset of the training samples for this comparison.
D.2 Baseline: Re-ranking using similarity scores of S alone
In Algorithm (see illustration in Fig, we directly leverage the similarity scores of S to rank Q class labels. Specifically, upon receiving an image I, C assigns all k possible classes with softmax scores. The set of top-ranked classes T and their respective scores is then selected for re-ranking. For each class, S compares a with the representative image (i.e., nearest neighbor) that class and generates a similarity score (from 0.0 1.0), indicating the likelihood that the two images belong the same calegory. The algorithm then reorders based on the scores generated by and gives the new top-1 predicted label g.
Algorithm Hard re-ranking using the ranking the similarity scores returned by alone
Inputs: Image Pretrained classifier C, Image comparator S, Number of top classes to consider Q. Output: New ranking of classes T' and top-1 label ÿ-
// Get initial predictions and scores from C
P,scores + C(x)
3: // Select top-Q classes and scores
4: T. scoressp + SelecctooQQUe scores, Q)
5: // List to store re-ranked classes with their final scores
6: R + empty list
for = to Q do
// Obtain nearest- neighbo explanation for the class
NN image RettieveeNe
T()))
10:
// Compute similarity score of S the class
11:
- Sax,NN ((__an__nage))
12:
// Update the final score for the class
13: Stinal + 8;
14: // Store the class and its respective final score
15: R.append[T[j]]] S(nna)
16: end for
17: // Re-rank the classes based on final scores
18: T ~ Sort R by final scores in descending order
19: // Get the new top-1 predicted class for C
20: 0 + First element of
21:
22: return T and
D.3 Soft re-ranking using product between scores of C and S
In Algorithm 2 we combine the scores from the pretrained classifier C and the comparator network S. For each image z, after C assigns initial scores the top Q classes, these scores are then recalculated by multiplying with the confidence scores generated by S. This algorithm represents more nuanced approach, leveraging the strengths of both C and S to improve the classification accuracy.
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green jay
0.99 green jay
0.95 blue jay
Comparator
0.70 indigo bunting
S
0.06 cape starling
0.01 painted bunting
40%
indigo bunting
Classifier
36%
green jay
C
11%
blue jay
Indigo bunting Di
10%
cape starling
blue jay
cape starlng
2%
painted bunting
painted bunling
Figure 11: Hard re-ranking. Re-ranking top-predicted classes of pretrained classifier C using the ranking of the similarity scores returned by S (C S). No Product of Experts.
Algorithm Soft Re-ranking using Product of Experts (C S)
Inputs: Image v, Pretrained classifier C, Image comparator S, Number of top classes to consider Q.
Output: New ranking of classes T' and top-1 label ÿ-
1: // Get initial predictions and scores from C
2: P, P.scores + C(x)
3: // Select top-Q classes and scores
T. scoressop + Topect scores, Q)
// List store re-ranked classes with their final scores
R emply list.
// Loop over top-Q classes in T and their scores
8: for 1 to Q do
9:
// Obtain nearest-neighhor explanation for the class
10:
NN MN_inage++ + Seettive.WerrestNiihhbbor T(i))
11:
// Compute similarity score S on the class
12:
S - NNN (e___ageee)) iimaee
13:
// Compute final score the product of initial class and similarity score of S
14:
Sfinal scorestopli]
15: // Store the class and its respective final score
16: R.append(T[i).snnnn
17: end for
18: // Re-rank the classes based on final scores
19: T Sort R by final scores in descending order
20: // Get the new top-1 predicted class for C
21: 8 First element of T
22:
23: return T and
We visually illustrate the algorithm in the Fig. 2
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E
Computational costs of image classifiers
Although our proposed classifier yields significant improvements in top-1 classification accuracy on tested datasets, it also introduces additional computational overhead beyond that the pretrained classifier C. To quantify the computational expense of our product-based classifier in Tab. we run each model (listed Tab. 12 times, processing 1,000 queries on a single NVIDIA V100 GPU.
Referring to Algorithm 2 the total runtime of our classifier can be represented as:
T TNNOO + TENN + Ts.
(3)
Here, TRNSO denotes the time taken by the classifier C (i.e., ResNet-50), TeNN represents the time required for nearest-neighhor retrieval, and Ts is the time taken by the image comparator S to compare the query image with all top-Q classes.
In Tab. 12 we find that our classifier is marginally slower than RN50, KNN, ProtoPNet, and Deformable- ProtoP However, surpasses these models terms of top-1 accuracy. Additionally, we note that our classifier significantly faster compared to correspondence-basee classifiers such as EMD-Corr and CHM-Corr, and it also demonstrates higher accuracy.
Table 12: Average runtime (s) of classifiers. Our re-ranking algorithm runs slower compared to C, kNN, and Proto-part classifiers, yet significantly faster than visual-corr classifiers.
Model
Runtime (s)
RN50
8.81 + 0.14
kNN
9.70 + 0.32
EMD-CorT
1927.69 + 17.48
CHM-Cort
6920.76 + 67.58
ProtoPNet
9.78 + 0.20
Deformable- Deformable-ProtoPPeet
9.98 + 0.26
S
46.04 + 0.04
RN50 xS
64.55 + 0.35
E.1 Improving runtime of C with reduced number of queries to image comparator
To mitigate the additional computational overhead introduced by image comparator S, we aim to reduce the number of queries to S by ignoring less probable labels. In Algorithm 2 for each input image, we set the queries to K 10, always examining the top-10 most probable classes. This is sub-optimal for model efficiency. Specifically, there are always classes receiving less than 1% probability by ResNet-50, which are unlikely to be the top-1 after re-ranking(see Fig. 2p. Reducing the number of K can significantly save computation with possibly minimal impact on accuracy.
answer this, we conduct an experiment on CUB-200 where, instead of re-ranking the entire top-10, we only ro-rank the classes that have a probability of 1% or higher assigned by the base classifier C. We call this method thresholding.
We found that:
C x S model performance on CUB-200 dropped very marginally by only 0.08% (from 88.59% to 88.51%).
The number of queries to image comparator S was reduced by approximately 4x (from 10 to about 2.5 queries per image).
This results in 2.5x speedup in the overall runtime of the S model (from 64.55 seconds to 28.95 seconds), as shown in the Tab. 13
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Table 13: Comparison of model performance and runtime with and without thresholding for re-ranking.
Model
Runtime (s)
Top-1 Acc (%)
RN50 xS
64.55 = 0.35
88.59
RN xS (with threshold)
28.95 # 0.11
88.51
E.2 Improving runtime of C with reduced training set during inference
The need for the entire training dataset test time for nearest neighbor retrieval is a limitation of our work. In this section, aim to study the impact of reducing the size of the training data inference-time performance. In particular, we shrink the training set from 100% to 50% and 33% of the original size and record the effect on the accuracy and runtime. The samples being removed are randomly selected from the training set.
Table 14: The top-1 accuracy and runtime of C x S on CUB-200 and Dogs- for different sizes of training data during inference.
Dataset
% Data
Samples per Class
Top-1 Acc (%)
Runtime (s)
CUB-200
100
30
88.43
64.55
CUB-200
50
15
88.26
59.70
CUB-200
33
10
88.
58.08
Dogs- 120
100
100
86.27
87.18
Dogs- 120
50
50
86.32
71.02
Dogs- 120
33
33
86.42
65.52
We found in Tab. 14 that reducing the size of the training data has little-to-no impact on the inferenco-time performance. When keeping the same accuracy, we can reduce the runtime by 10% on CUB-200 and 24.9% on Dogs-120 by reducing the training set to 33% of the original size. It is an interesting research question to determine the smallest training set size that still maintains the same accuracy. However, as this question is orthogonal the main focus of our work, we opt leave it for future work.
F Sanity checks for image comparator S
F.1 Controlling nearest-neighbor explanations
Considered datasets: CUB-200, Cars-196, and Dogs-120. Considered tasks: Binary image classification.
In this section, investigate the logical consistency of S. Specifically, evaluate the model's ability to correctly distinguish images that are the same and different. This is achieved by comparing S's responses when the NN is the input query itself (expected output: 1 or Yes) and when the NN is randomly sampled image (expected output: 0 or No). When sampling the random NN, we study two scenarios: one where NNs are assigned random values and another where they are random real images. These random real images are sampled by shuffling the NNs in image pairs inside a batch size of pairs. For image comparators S, we use NNtturalist-pretraan Dogs- 120.
ResNet-50 for CUB-200 and ImageNet-pretraite
As seen in Tab. 15 when the NN provided is the input image, S correctly recognizes the two images as being from the same class, yielding a Yes ratio of 100%. On the other hand, when a random-valued NN is used, the comparator network correctly identifies that the images are not from the same class, yielding low Yes ratios, namely 3.51%, 2.01%, and 1.86% for CUB-200, Cars-196, and Dogs-120 respectively. The ratios represented by Yes for random-real images are also low for both datascts, as expected. This verifies the logical consistency of S.
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Table 15: Logical tests for the image comparator by controlling nearest-neighbor explanations. These tests involve measuring the ratio (%) at which perceives x and its nearest neighbor nn as belonging to the same class. performs as expected in these sanity checks. Images with random values, being disttriiiiti samples for S, sometimes lead to incorrect outputs.
Explanation
CUB-200 (%)
Cars- 196 (%)
Dogs-122 1ogs-120 (%)
Query
100
100
100
Random values
3.51
2.01
7.93
Random real images
0.71
0.79
1.86
F.2 Comparator tends be more confidence when it is correct than when is wrong
Considered datasets: CUB-200. Considered tasks: Binary image classification.
In analyzing the performance of image comparator S on CUB-200, we observe intriguing insights into its similarity scores. In particular, we are interested in 4 types of classifications made by S in the binary (Accep- t/ /Reejctt) task: Correctly Accopting, Incorrectly Accepting, Correctly Rejecting, and Incorroctly Rejecting the top-1 predicted label of am iNaturalist- iNaturalist-pretrained ResNet-50 classifier. The average confidence scores for these cases are as follows: "Correctly Accept 99.08%, "Incorrectly Accept" 91 91.85%, "CorrectlyRejeet 93.50% and "IncorreettlyRejeet""
87.09%.
These numbers reveal nuanced understanding of the image-comparator network's inner- workiwwor.nngs More specifically, S demonstrates higher scores in its correct decisions (either Accept or Reject) compared to the incorrect ones. This characteristic makes S compatible for re- re-weighting initial predictions of a classifier C using S scores. When the comparator accepts a label, it is more likely to be groundtruth, and conversely for rejections, thus allowing effective up-weighting for correct labels and wein-weighting for incorrect labels.
This observation inspires to update confidence scores initially given by pretrained classifier C using product of model scores. Formally, for any given class A, the pretrained classifier C assigns confidence score, denoted SCA, and the comparator network S assigns score S5 The final score 8 for the class is updated to:
8 8CA x 88A
(4)
We incorporate this product of scores in Algorithm
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G Maximum possible accuracy with re-ranking algorithms
In this section, we present the maximum achievable accuracy of classifiers C in Tab. 2 the potential of re-ranking almsrithms like the one illustrated in Fig 2
3 and f to explore
Surprisingly, by merely considering the top-1 and top-2 probable classes, we consistently observe accuracy enhancements ranging from - 9 pp in Tab. I6 As we extend our consideration to higher-ranked categories, the gains in accuracy plateau, ultimately approaching near-perfect accuracy at the top 10.
Table 16: Maximum achievable classification accuracy (%) with RN50 classifiers in Tab. and Re-ranking algorithms can achieve up to approx. 99% when considering the top-10 predicted labels with highest scores. The results indicate significant potential for improving classification accuracy via re-ranking top-predicted classes like Fig. 2
Top-Q
CUB-200
Cars-196
Dogs-122 120
85.86
$9.73
85.84
2
93.10
96.18
94.59
3
95.53
97.77
97.10
4
96.84
98.48
98.09
5
97.48
98.79
98.74
6
97.84
98.99
99.04
7
98.10
99.12
99.22
8
98.34
99.19
99.30
9
98.50
99.29
99.36
10
98.64
99.38
99.49
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H Evaluating probable-class nearest-neighhor explanations on humans
We invostigate whether PCNN explanations, learned by the image comparator S, offer more assistance to humans than traditional top-1 nearest neighbors Nguyen et al. (2021); ; Taesiri et al. (2022) in the binary decision-making task. For simplicity, we refer to these as the top-1 and top-Q experiments.
Query samples For each of the top-1 and top-Q experiments, we select 300 correctly classified and 300 misclassified query samples determined by CUB-200 RN50 S, amounting to a total of 600 images. From this pool, a human user presented with a randomly chosen subset of 30 images.
Visual explanations In the top-Q experiment, retrieve nearest-neighbor prototype from each of the multiple top-ranked classes determined by C x S and then present these to human participants (see Fig. 14]. By contrast, in the top-1 experiment, human users are only shown the nearest neighbors from the single top-1 predicted class (see Fig. 13/. In both experiments, participants are presented with the input image alongside nearest neighbors. Their task remains the same: to accept or reject the top-1 predicted label from x S utilizing the explanations provided. To minimize potential biases, we do not display the confidence scores for the classes.
There 35 voluntary participants in the top-1 experiment and 25 in the top-Q experiment, respectively. Although we do not implement formal quality control measures, such as filtering out malicious users, we encourage participants to perform responsibly. Moreover, we consider only the data from participants who complete all 30 test trials.
Participants
Study interface Prior to the experiments, participants are briefed on the task with instructions as detailed in Fig. 12 Each user is tasked with 30 binary questions for an experiment. You can also try out yourself the human experiment interface this link
Instructions
trained to recognize the most salient bird in a photo. In this study, you will team up with Sam. You will see his decisions and explanations per image and decide whether you agree (Yes) or disagree (No) with Sam's predicted label.
Sam is robot
Input
Agree
I'm 100% confident this is Painted Bunting
Disagree
Sam
You
Explanation Info Businn
Brager
Bxcdd Conrara
1. Please perform this study on a computer (not a phone) and do not get help from the Internet or other people.
2. Please make your best of efforts in using the explanation to judge Sam predicted label. You can compare the Input image vs. the prototype images of relevant birds (5 birds in the explanation).
Figure 12: The experiment instructions shown to voluntary participants.
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Accept/Reject Al predicted label using Explanations
Username
Images
Total Images
Type
me u2hisalu
30
topt
Sam guessed the Input image is Painted Bunting with 10% confidence. this bird Painted Bunting?
YES
Accept/Reject Al predicted label using Explanations
Images
Type
30
Inst
Sam guessed the Input image is Tropical Kingbird with 100% confidence. this bird a Tropical Kingbird?
YES
NO
Accept/Reject Al predicted label using Explanations
Usemame
Labeled Images
Tocal Images
Type of NNs
u2h1salu
Sam guessed the Input image Myrtle Warbler with 90% confidence. Is this bird Myrtle Warbler?
YES
NO
Figure 13: Example test trials given human users for the top-1 experiment. Nearest neighbors are retrieved from the top-1 predicted class of CUB-200 ResNet-50 classifier.
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Accept/Reject Al predicted label using Explanations
Username
Labeled Images
Total Images
Type
topQ
Sam guessed the Input image is Brewer Blackbird with 17% confidence. Is this bird a Brewer Blackbird?
test
Accept/Reject Al predicted using Explanations
Username
Labeled
Total
1onmame-73::0jpn
topQ
Sam guessed the Input image is Indigo Bunting with 100% confidence. th bird Indigo Bunting?
YES
Accept/Reject Al predicted label using Explanations
Images
Images
Ueeramme77Se0onn
12
30
tepQ
Sam guessed the Input image is Purple Finch with 99% confidence. Is this bird a Purple Finch?
YES
NO
Figure 14: Example test. trials given human users for the top-Q experiment. Nearest neighbors are retrieved from the multiple top-ranked classes of CUB-200 ResNet-50 classifier.
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find that showing human participants with probable-class nearest-neighhor explanations enhances their accuracy by around +10 pp, boosting it from 54.55% (+ 9.54) to 64.58% (+ 8.06), in the task of distinguishing correct from incorrect classifications made by the classifier C x S Additionally, we observe that participants with top-1 explanations tend to accept predictions at a very high rate of up to 85.15%, inline with prior studies indicating that. visual explanations can increase human trust, yet may also result in higher false positive rates Nguyen
Main findings In Tab. 17
(2021): Fok
Weld (202)).
In contrast, those with top-Q explanations accept predictions at lower 64.81%. This trend in acceptance rates correlates with the performance patterns in Fig. g where top-1 participants exhibit high accuracy with correct AI decisions and lower accuracy with incorrect ones.
Table 17: Human accuracy (%) upon Al correctness test samples for top-1 and PCNN and data statistics.
Explanation
AI Correctness
μ4 (%)
(%)
Numb. of Samples
top-1
AI is Wrong
22.28
15.02
525
top-1 Overall
AT is Correct
90.99
7.73
465
54.55
9.54
990
PCNN PCNN
AI is Wrong
49.31
17.27
404
AI is Correct
79.78
14.46
406
Overall
64.58
8.06
810
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Visualizations
Visualization of top-1 corrections via re-ranking in single-label image classification
We present cases where C S corrects the top-1 predictions made by pretrained classifiers C on CUB-200 (Figs.
15
Cars-196 (Figs. 19
22
Dogs-120 (Figs. 23
26
Initial class ranking pretrained classifier
Query: Least Tem
Top Forsters Tern
Top2: Least Tem
Top3: Common Tern
Top4: Elegant Tern
Top5: Homeed Puffin
RN50: 61% 5: 0.40
RM56: 37% S: 0.95
RN5O: 0% s: 0.00
RN50: 0% 5: 0.01
RN50: 0% 5: 0.00
Refined class ranking by Product of Experts C x S
Top1:
Top3: Flegant
Hom Puffin
RN50 x S. 35%
RA50 x 24%
RASO 5%
ANSOO x S: 0%
RNSO S: 0%
Figure 15: pretrained model C makes predictions on the Query image (ground trruth label: Least Tem) and produces initial ranking (top row) but the top-1 prodicted class (Forsters Tem) does not match the ground-truth label. Our classifier C S compares the query image with the representative of each class (the first NN example in each class) and uses Product of Experts C x S to re-rank those classes. The refined class ranking is presented in the bottom row where the Least Tem class has been recognized top-1.
Initial class ranking by pretrained classifier
Query: Purple
Topl: Hummingbird
Sparrow
Spotted
ANS0: 85% S: 0.00
AMSO: 12% St 1.00
RNSO: 1% S: 0.00
RNSO: 0% St 0.00
RNSO: 0% S: 0.00
Refined class ranking by Product of Experts C x S
Top Purple Finch
Top2: Anna Hummingbird
Top Song Sparrow
Top4: Spotted Catbird
Top5: Winter Wren
RNSO 12%
ARSO S: 0%
ARSOX 0%
FINSO x S: 0%
RASO S: 0%
Figure 16: A pretrained model C makes predictions on the Query image (ground-truth label: Purple Finch) and produces initial ranking (lop row) but the top-1 predicted class (Anna Hummingbird) does not match the ground-tren truth label. The refined class ranking is presented in the bottom row where the Purple Finch class has been recognized top-1.
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Initial class ranking by pretrained classifier
Query: Bunting
Topl: Indigo Bunting
Top2: Grosbeak
Top3: Bunting
Top4: Florida Jay
Top5: Tennessee
KN50: 57% 1: 0.00
ANSC: 24% T 0.36
RM50: 16% 6,98
MN50: IN 0.00
KI50: 0% S: 0.00
Refined class ranking Product of Experts C S
Top1: Lazull Bunting
Top2: Blue Grasbeak
Top3: Indigo Bunting
Top4: Florida Jay
Top5; Tennessee Warbler
16%
RASO 0%
Figure 17: A pretrained model C makes predictions on the Query image (ground-truth label: Lazuli Bunting) and produces initial ranking (top row) but the top-1 predicted class (Indigo Bunting) does not match the ground-truth label. The refined class ranking is presented in the bottom row where the Lazuli Bunting class has been recognized as top-1.
Initial class ranking pretrained classifier
Query: Pine Warbler Top 1: Yellow throsted Virea
Top2: Pine Warbber
Top3: Blue winged Warbler
Top4: Orchard Oriale
TapS: Hooded Warbler
RNSO: Refined class ranking by Product of Experts x
SS: 1.00
S:
0%
St
Top1: Pine Warbler
Top2: Yellow throated Vireo
Top3: Orchard Oriole
Top4: winged Warbler
Hoeded Warbler
RH50 5: 40%
RNSO X 5.00%
X 5.0%
RN50 x 5: 0%
AN50 5: 0%
Figure 18: A pretrained model makes predictions on the Query image (ground-truth label: Pine Warbler) and produces initial ranking (top row) but the top-1 predicted class (Yellow throated Vireo) does not match the ground-truth label. The refined class ranking is presented in the bottom row where the Pine Warbler class has been recognized top-1.
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Initial class ranking by pretrained classifier
Query Susrai Sodan 2007
Tapl: Sucull 2044 Sudien 2012
Tapk: Suave Auto Sidden 2007
Top3: ihsundaal Marcuue se SVV 2012
Tack Nassen Laed teiiibbt 2012 Tapls Misar IV "uavanger 2012
CUQUE AQUI
VER IMAGEN ORIGINAL
RN50: 20% S: 0.00
ANSC: 16% S 1.00
RNSO: S: 0.00
ANS0: 6% S: 0.00
RN5O: 3% 0.00
Refined class ranking Product of Experts C
Tepil Sucuki Sosan
hpp Sumak 2013
NpSi Nasson Hotuntao 2012
hea rosartia Veroour 2012 Taps Nidas NV fosienger van 2012
CLIOUE AQUI
VER IMAGEM ORIGINAL
16%
5.0%
RNSO
RINSO S:
Figure 19: A pretrained model C makes predictions the Query image (ground-truth label: Suzuki Aerio Sedan 2007) and produces initial ranking (Lop row) but the top-1 predicted class does not match the ground-truth label. Our classifier C x S compares the query image with the representative of each class (the first NN example in each class) to re-rank those classes basod on confidence scores. The refined class ranking is presented the bottom row where the Suzuki Aerio Sedan 2007 class has been recognized as top-1.
Initial class ranking by pretrained classifier
Metar lischacc
Tapl: Ketan laathaakk
Top XK
Tpl: Medan lbachbad bade Ponche Sedan
Soylar Cerverible 2019
3%
Refined class ranking by Product of Experts C
Hotmbbrk
Sayler Corveriste
12%
KI50 x 5: 0%
RNSO 5.0%
x KNSO 5:
Figure 20: pretrained model C makes predictions on the Query image (groud--trin -tuth label: Nissan Leaf Hatchback 2012) and produces initial ranking (top row) but the top-1 predicted class does not match the ground-truth label. The refined class ranking is presented in the bottom row where the Nissan Leaf Hatchback 2012 class has been recognized as top-1
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Initial class ranking by pretrained classifier C
Query Viko Scse SUN 2000
Vol: HMWKS SUW 2007
Test: Wire X053 SIV22007
Til:: Festley Artage Sedbo 2008
Tep-: BMW X0 STV 2012
Tep.: ees Grand Chersien suy 2012
RN50: Refined class ranking by Product of Experts C
RN50: 1.00
RA50:
RN50: 0.00
RAS
Tea' Whk S.V 2007
Tea BMN SUV 2007
Yol MMN X3 SUV 2012
Tend Berthey Amooge Sesan 2005 with zea Crand Chersten 2012
UPG
7%
RN50
Figure 21: A pretrained model C makes predictions on the Query image (ground-truth label: Volvo XC90 SUV 2007) and produces initial ranking (top row) but the top-1 predicted class does not match the ground-truth label. The refined class ranking is presented in the bottom row where the Volvo XC90 SUV 2007 class has been recognized as top-1.
Initial class ranking pretrained classifier C
5% S:
S:
Refined class ranking by Product Experts
Mortis
Figure 22: A pretrained model C makes predictions on the Query image (ground-truth label: Ferrari Coupe 2012) and produces initial ranking (top row) but the top-1 predicted class does not match the ground-truth label. The refined class ranking is presented in the bottom row where the Ferrari FF Coupe 2012 class has been recognized as top-1.
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Initial class ranking by pretrained classifier
Query:
Top Groenendael
Top2: Retriaver
Top3: Newffoundland
Top4; Border, Collie
Top5:
KN50: 63% 0.0.0
ANSG, 20% I 0.05
KN5C 4% 5: 0.00
AN50: 5% 0.82
KN50: 1% St 0.00
Refined class ranking Product of Experts C
Topl: Border Collie
Top2: Flat Coated Retriever
Top3: Groenendsel
Top4: Newfoundland
Top5: Colle
Figure 23: A pretrained model C makes predictions on the Query image (ground-truth label: Border Collie) and produces initial ranking (top row) but the top-1 predicted class does not match the ground-truth label. Our classifier C x S compares the query image with the representative of each class (the first NN example in each class) to re-rank those classes based on confidence scores. The refined class ranking presented in the bottom row where the Border Collie class has been recognized as top-1.
Initial class ranking by pretrained classifier
Query: Toy Terrier
Topl: Italian [ Greyhound
Top2: Toy, Terrier
Top3: Walker, Hound
Top4: Basenii
Top5: Mexican Hairless
S: Refined class ranking by Product of Experts
Top Toy. Tarrerr
Top2: italian,, Greyhound
Top3: Maxican Hairless
Top4: Walker Hound
TapS: Basenji
x 13%
RNSO
RNSO
Figure 24: pretrained model C makes predictions on the Query image (ground-truth label: Toy Terrier) and produces initial ranking (top row) but the top-1 predicted class does not match the ground-truth label. The refined class ranking is presented in the bottom row where the Toy Terrier class has been recognized as top-1.
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Initial class ranking by pretrained classifier
Query: Otterhound
Top1:
Top2: Nortelk Tarrer
Top3: Otterhound
Top4:
Top5:
RN50; 36% S: 0.16
RNSC. 30% S 0.01
RM50: 15% S. 0.85
RNS0: 2% 5: 0.41
RN50: 1% S: 0.00
Refined class ranking by Product of Experts C
Top1: Otterhound
Top2: Irihh Terrier
Top3: Lakeland Terrier
Top4: Norfolk Terrier
Top5: Border Terrier
RN50 5: 13%
KINOO 5.5%
AN50 5.18
RN50 0%
ANSO 5: 0%
Figure 25: A pretrained model C makes predictions the Query image (ground-truth label: Otterhound) and produces imitial ranking (top row) but the top-1 predicted class does not match the ground-truth label. The refined class ranking presented in the bottom row where the Otterhound class has been recognized as top-1.
Initial class ranking pretrained classifier
Sealyham Terrier
Topl: Shih Tzu
Top2: Dandie Dinmont
Top3: Sealyham Terrier
Japanese Spaniel
Top5: Pekinese
KN50: S: 0.00
KN50: S: 0.17 Refined class ranking by Product of Experts C
KNSO: 4%
MNS0: 0.00
KN50: 3% S:
Top1: Sealyham Terrier
Top2: Pekinese
Top3: Dinmont
Shin Tzu
RNSO x. 4%
RNSO x 2%
RNSO x St 0%
ANLO x
RX5O S: 0%
Figure 26: pretrained model C makes predictions on the Query image (ground-truth label: Sealyham Terrier) and produces initial ranking (top row) but the top-1 predicted class does not match the ground-truth label. The refined class ranking is presented in the bottom row where the Sealyham Terrier class has been recognized top-1.
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1.2 Visualization of image comparator S's errors
The classification performance of S on C's correct/incorrell predictions varies from 90% to 92% for both datasets as described in Sec. B.7 We are keen to investigate the cases where S does not perform accurately to better understand the limitations of them.
We find that S incorrectly predicts that two images are from the same class, the image pairs often indeed appear to be very similar. Via this analysis, we are able find multiple examples of two identical images which had two different labels (i.e., wrong annotations in Cars-196).
In contrast, when S incorrectly predicts that two images are dissimilar, the image pairs display notable differences (e.g., due to different lighting angles). Qualitative results can be found in Fig. 27 for CUB-200 and Fig. 28 for Cars-196.
Query: 378, Warbier
The predicted: 195,Canitee
Query: 142.8lack
Top-1 preduten 147 Tere
Similarity score of 1.00
Similarity score of 0.99
Similarity 0.01
Similarity
(b)
Figure 27: Failures of an image comparator S (94.44% test acc) CUB-200. (a) incorrectly accepted the C's predictions for both images. For the left image, the two birds (Swainson Warbler vs. Carolina Wren) appear similar. For the right image, the birds (Black Tem vs. Least Tern) also appear visually similar. Both scenarios led S to accept with high confidence. (b) S incorrectly rejected the C's predictions for both images. For the left image, the bird Anna Hummingbird. For the right image, the bird a Elegant Tern. Both sets of birds show inter-class variations, leading to mistakenly reject the predictions.
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Query. Andi Hetchback
Top- prodicted: Coups
Query: Parrart 458 Italle Coups 2012
Tap3 predicted: Parrart itall Convertible 2012
Similarity score of 1.00
Similarity score of 1.00
(a)
Oveny: AM General Hummer SW 2009
Top-2 prodicted AM General Hummer SUV 2000
Overy: Acura RL Sodan 2012
Top- predicted: Acura EL Sedan 2012
COST
Similarity score of S: 0.00
Similarity score of S: 0.47
(b)
Figure 28: Failures of an image comparator S (95.02% test acc) on Cars-196. (a) S incorrectly accepted the C's predictions for both images. We discovered that the cars are indeed identical. The image comparator's misclassifiattins are due to errors in Cars-196 dataset annotations. (b) incorrectly rejected the C's predictions for both images. We observed that, in most instances, the cars display noticeable differences, such color, viewing angle, and lighting.
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Ouang Appensafer
Top-1 predicted Entiebucher
Quany
Top predicted: Brabeacon Grifton
Similarity score of S: 87.00
Similarity score of S: 55.00
(a)
Query: Labrador Recrivver
Tap-1 predicted: Labrador Jutriever
Qvery: 5Nth TrU
Top-1 predicted SNA Tea
Similarity score of S: 46.00
Similarity score of S: 7.00
(b)
Figure 29: Failures of an image comparator S (92.05% test acc) on Dogs-120. (a) incorrectly accepted the C's predictions for both images. For the left image, the two dogs (Appenzeller vs. Entlebucher) appear similar. For the right image, the dogs (Chihuahua vs. Brabancon Griffon) also appear visually similar. Both scenarios led S to accept but only with low confidence. (b) incorrectly rejected the C's predictions for both images. For the left image, the dog is Labrador Retriever. For the right image, the dog Shih Tzu. Both sets of dogs show variations in light and pose, leading S to mistakenly reject the predictions.
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1.3 Visualization of nearest neighbors
We show the nearest neighbors nn utilized for forming training pairs in training S in Figs. 30 & 31 for CUB-200, Fig. 32 & 33 for Cars-196, and Fig. 34 & 35 for Dogs-120. From the top-1 predicted classes, we assemble Q pairs. For the remaining classes beyond the top-1, we take the first ncarest neighbor within cach class and combine with the respective query sample. With Q 10, a total of 19 pairs are returned by the sampling algorithm (Fig.
When we prompt the pretrained model C make predictions on its training data x, it reasonable to expect the top-1 predicted class match the ground-truth label. This expectation is grounded in the fact that model C typically exhibits nearly perfect accuracy (around 100%) on its training set.
NAIT TEEAP TATH
(a)
Overy OS2. Willed_Gieedeeeeeeeee
Tep-31 252,Pied bileed Grabe
(b)
Figure 30: Training pairs for CUB-200 image comparator. For a given query, pretrained model C predicts the label Field Sparrow (a) and Pied Billed Grebe (b), which align with the ground-truth labels. Subsequently, from each of these query images, generate 10 positive pairs (using NNs of top-1 class in the middle row) and 9 negative pairs (using NNs of top-2 to 10 classes in the bottom row).
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Query: Chipping Sparron
Topel: 316, Spereen Sperrow
California dull
(b)
Training pairs for CUB-200 image comparator. For a given query, pretrained model C predicts the label Chipping Sparrow (a) and California Gull (b), which aligı with the ground-truth labels. Subsequently, from each of these query images, we generate 10 positive pairs (using NNs of top-1 class in the middle row) and 9 negative pairs (using NNs of top-2 to 10 classes in the bottom row).
Figure 31:
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Query: Sunuki SX4 Hatch back 2012
Tep-l: Suruki Hatchback 2012
Query:
Top-1: Ilyundal Amera Saden
(b)
Figure 32: Training pairs for Cars-196 196 image comparator. For a given query, a pretrained model predicts the label Suzuki SX4 Hatchback 2012 (a) and Hyundai Azera Sedan 2012 (b), which match the ground-truth labels. Subsequently, from each of these query images, we generate 10 positive pairs (using NNs of top-1 class in the middle row) and 9 negative pairs (using NNs of top-2 to 10 classes in the bottom row).
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Quary: Ford Freactur Minivan 2007
Top-1: Ford Freester Minivan 2007
Query: Dodge
Dedge Carvven Minivann 1697
(b)
Figure 33: Training pairs for Cars- 196 image comparator. For a given query, a pretrained model predicts the label Ford Freestar Minivan 2007 (a) and Dodge Caravan Minivan 1997 (b), which match the ground-truth labels. Subsequently, from each of these query images, we generate 10 positive pairs (using NNs of top-1 class in the middle row) and 9 negative pairs (using NNs of top-2 to 10 classes in the bottom row).
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Query: Chibushus
Tep-l: Chibuahus
Query: Japanese
Top-1: Japansse Spantal
(b)
Figure 34: Training pairs for Dogs-120 image comparator. For a given query, a pretrained model predicts the label Chihuahua (a) and Japanese Spaniel (b), which match the ground-truth labels. Subsequently, from each of these query images, generate 10 positive pairs (using NNs of top-1 class in the middle row) and 9 negative pairs (using NNs of top-2 to 10 classes in the bottom row).
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Query: Rhodesian Ridgeback
Top-1: Rhodesian Ridgeback
Query: Engllish
Top-1: glllih Fachound
(b)
Figure 35: Training pairs for Dogs-120 image comparator. For a given query, & pretrained model predicts the label Rhodesian Ridgeback (a) and English Foxhound (b), which match the ground-truth labels. Subsequently, from each of these query images, generate 10 positive pairs (using NNs of top-1 class in the middle row) and 9 negative pairs (using NNs of top-2 to 10 classes in the bottom row).
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Reviews:
[{"review_id": "NKJFdIu1TD", "reviewer": "Reviewer_96cH", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n1.  The proposed method, PCNN, enhances the accuracy of a fine-grained image classification system without the need to retrain the underlying black-box classifier. \n2. The PCNN method achieves state-of-the-art classification accuracy on benchmark datasets such as CUB-200, Cars-196, and Dogs-120. \n3. The human study conducted in the research reveals that presenting PCNN to users improves their decision-making accuracy compared to the conventional practice of displaying only the top-1 class examples.\n\nWeaknesses:\n\n1. When compared to other existing classifiers, the propsed method is slower than k-NN and prototypical part-based classifiers but quicker than CHM-Corr and EMD-Corr re-rankers. Although it is in line with existing methods, its slower speed could be a drawback in situations where efficiency is crucial.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The study focuses on leveraging nearest neighbors (NN) to enhance predictions made by a pretrained classifier. The authors propose a method that employs an image comparator, labeled as S, which serves two crucial functions. Firstly, it compares the input image with NN images from the top-K most probable classes. Secondly, it utilizes the output scores from S to weigh the confidence scores of the pretrained classifier, denoted as C. Through this approach, the researchers consistently demonstrate improved accuracy in fine-grained image classification across datasets such as CUB-200, Cars-196, and Dogs-120. Moreover, the paper incorporates a human study wherein lay users are presented with the nearest neighbors of probable classes instead of solely the top-1 class examples.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1.  Include a discussion on potential strategies to address the challenge of extended inference time.\n\n2. Can you determine an optimal value for K by considering the unique characteristics of each query or input, in order to enhance the balance between accuracy and runtime efficiency?\n\n3. Is it possible to implement incremental learning techniques to update the comparator S over time with new data?", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "yHhJhS8Br3", "reviewer": "Reviewer_bLsM", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths\n\n**Presentation**: Well written and structured, easy to follow.  All figures\nare readable.  I did not find any major linguistic issues.\n\n**Novelty**:  PCNN is, to the best of my knowledge, novel.  It shares many\nsimilarities to existing kNN and prototype-based approaches, but it is overall\noriginal.\n\n**Significance**:  PCNN appears to attain better classification performance\nthan existing data sets on a challenging data set (CUB-200).  The idea of \n\n**Quality**:  The proposed technique (PCNN) is rather simple but also\ntechnically sound.  The experimental setup of the machine experiments is rather\nexhaustive and indicates PCNN can lead to better machine performance.  The user\nstudy seems to be setup appropriately (but it should probably be double checked\nfor confounding by an expert, e.g., a behavioral psychologist).\n\n**Related Work**: Coverage of related works is good and these are portrayed\nfairly.\n\n\nWeaknesses\n\n**Presentation**:  The message comprises two disconnected pieces: 1) showing\nexplanations for the top-K classes aids human decision making, 2) reranking\npredictions using NNs helps machine performance.  This is not a big deal, but a\nmore streamlined message would have helped.  Another issue is the method name\nis not used consistently throughout the text.  For instance, it is sometimes\ncalled C \\times S, sometimes PoE; sometimes PCNN is an architecture, sometimes\na new type of explanation; this is a bit confusing.  I would prefer if the\nauthors used PCNN everywhere, for simplicity.\n\n**Quality**:  I don't think the paper is entirely open about all its\nlimitations, see below.\n\n\n\n\n**TL;DR**: okay paper that builds on a nice idea, but that also requires more\n  emphasis on limitations.\n\nOn the plus side, I think the paper is well written and worth accepting.\n  I like the main intuition for the user study -- namely that providing\n  (almost) counterfactuals to users can lead to more careful decision\n  making -- although I don't think this is entirely in line with the\n  experimental results.  The proposed AI pipeline is sensible and I do not\n  have particular issues with it.  The main issue is that some key limitations\n  are not discussed properly, see below.  The experiments are rather thorough\n  and consider a number of research questions, competitors, ablations, ...", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors consider using nearest neighbor explanations (NNEs) of an image\nclassifier's top-K classes for improving (1) human decision making accuracy,\nand (2) the model's accuracy. The second point is studied using a pipeline in\nwhich the NNEs are used for reweighting/reranking the model's predictions\nthemselves, denoted PCNN.  Reranking is implemented by multiplying together the\nprobability that the input lies in a class y with the probability that the 1-NN\nexample belongs to the same class.  This is predicted by a CrossViT-based\n``comparator'' fed with the input and the 1-NN.  Point (1) is motivated by the\nfact that showing users only the 1-NN explanation is potentially limiting, and\nin fact can hinder human performance in user studies.  Point (2) stems from a\ndesire to apply a similar logic to machine decisions.  The results highlight\nhow PCNN improves machine performance over various competitors on three\nfine-grained image classification data and human performance on CUB-200.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- The authors are upfront about the fact that PCNN requires training data at\n  test time (like all kNN-based predictors), but do not list this as an actual\n  limitation in Section 5, while I think it is.\n\n  There they mention run-time of PCNN is longer than other competitors, but the\n  main issue is PCNN requires the training data to be available in entirety --\n  or, at least, the experiments do not study the impact of reducing the sice of\n  the training data on inference-time performance.  This is a clear downside\n  compared to, say, ProtoPNets, which memorize relevant (part) prototypes\n  instead.\n\n  So there exists a clear trade-off between space and time requirements and\n  prediction improvements (which is substantial but not huge to begin with,\n  usually in the order of 2-3% top-1 accuracy over the runner-ups, at least\n  according to Tables 3-5).\n\n- I don't think the results of the user study are reported appropriately in the\n  introduction.\n\n  Bottom line: showing more NNs to users makes them more skeptical, meaning\n  they end up underestimating machine perforance.  This should be clearly\n  stated in the introduction, at the bare minimum.  Instead, the authors focus\n  on the benefits of PCNN only, and write: \"A 60-user study finds that,\n  compared to showing top-1 class examples, PCNN improves user performance on\n  the distinction task by almost +10 points (pp) (54.55% vs. 64.58%) on CUB-200\n  (Sec. 4.6).\"  I don't think this is entirely fair and the text should be\n  amended.  This should also be listed in the Limitations section.\n\n- The construction of the training set for S assumes C is already reasonably\n  high-quality: is this always a reasonable assumption?  Please add this to the\n  Limitations section too.\n\n- An analysis of errors introduced by the reranking step would have been useful.\n\n- p 4: \"We empirically test K = {1, 3, 5, 10} and find K = 10 to be optimal.\"\n  The fact that the optimal value is at the very end of the spectrum begs the\n  question whether increasing K could improve performance further.  Did the\n  authors evaluate what happens for larger values of K?  Clearly, increasing K\n  would not be ideal for human decision making, but it should not consistute\n  for PCNNs proper.\n\n- p 2 onwards: The authors say their model is a Product of Experts (PoE), based\n  on the definition of Hinton, 1999.  Reading through this reference, however,\n  I get the impression that in PoEs the various distributions are conditionally\n  independent given the input (for instance, in p 4 of Hinton '99, they state\n  \"the hidden states of different experts are conditionally independent given\n  the data\").  The same (conditional) independence assumption seems to be\n  instrumental in more recent research on PoEs, see:\n\n    Gordon et al., \"Identifiability of Product of Experts Models.\" 2024.\n\n  To me, conditional independence seems necessary to reinterpret the product of\n  distributions as a factorization of a more complex joint distribution, which\n  lies at the heart of PoEs.\n\n  However, independence does not appear to hold for the CSP model.  I would\n  appreciate if the authors could clarify this point, and -- if independence is\n  indeed a prerequisite of PoEs -- changed their wording accordingly.\n\nI am willing to change \"Claims and Evidence\" to \"Yes\" if the authors expressly discuss these limitations in the relevant section.", "broader_impact_concerns": "Broader impact should briefly discuss the potential impact of manipulating (in the case of PCNN, lowering) user confidence in machine predictions in, especially in **time critical** high-stakes scenarios.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "RXWp1aoeve", "reviewer": "Reviewer_g7wm", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n1. Novel and effective approach to leverage nearest neighbours for improving classification accuracy without retraining the original model.\n2. Extensive experiments across multiple datasets and architectures, demonstrating the robustness and generalisability of the proposed method.\n3. Thorough hyperparameter tuning and ablation studies to validate design choices.\n4. Comprehensive comparisons with existing methods, showcasing the superior performance of the proposed approach.\n5. Inclusion of a human study to evaluate the interpretability and usefulness of PCNN explanations for human decision-making.\n\nIt is worth highlighting that the authors have included several comprehensive appendixes showing some additional results and implementation details\n\n-----------------------------------------------------------\nAs far as I can understand the nitty gritty of the method, the main weaknesses seem to be the following:\n\n1. The re-ranking process introduces additional computational overhead, making the PoE model slower than the original classifier C.\n2. The method relies on the availability of a well-trained comparator S, which may not be feasible or practical in certain scenarios.\n3. The paper does not provide a theoretical analysis or justification for the effectiveness of the proposed approach.\n4. The human study is limited to the CUB-200 dataset, and the generalizability of the findings to other domains is not explored.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposes a novel method called \"Probable-Class Nearest Neighbours (PCNN)\" to improve the accuracy of fine-grained image classification models without retraining the base classifier.\nPCNN creates an explanation set containing the nearest neighbour images from the top K predicted classes by the base classifier. These PCNN examples are used along with an image comparator model to re-rank the predictions of the base classifier.\nThe image comparator is a binary classifier trained to compare pairs of images and predict if they belong to the same class or not. It is trained on positive (same class) and \"hard\" negative (different but visually similar classes) image pairs sampled using the predictions of the base classifier.\nThe PCNN method improves classification accuracy across three fine-grained datasets (birds, cars, dogs) and multiple base classifier architectures like ResNets. It outperforms prior prototype-based explainable models.\nA well-trained image comparator can be combined with arbitrary unseen base classifiers to boost their accuracy, without requiring retraining of the comparator.\nIn a human study, showing PCNN examples improved user accuracy by ~10% compared to just showing the top predicted class examples, which often misled users on visually similar categories.\nThe tradeoff is that the PCNN approach adds inference time overhead compared to just using the base classifier alone, due to the need to query the image comparator multiple times.\n\nSummary of Contributions:\n\n1. Proposed a novel explanation method called Probable-Class Nearest Neighbours (PCNN), which consists of K nearest images from the top-K predicted classes, to improve fine-grained image classification accuracy for both AI and humans.\n2. Introduced a re-ranking algorithm that leverages an image comparator network (S) trained on PCNN to weigh and refine the predictions of a frozen, pretrained classifier (C), forming a Product of Experts (PoE) model.\n3. Demonstrated consistent accuracy improvements of the PoE model over the original classifier C across three fine-grained image classification datasets (CUB-200, Cars-196, and Dogs-120) and multiple architectures (ResNet-18, ResNet-34, and ResNet-50).\n4. Showed that a well-trained comparator S can be effectively combined with arbitrary, unseen classifiers C to boost their performance.\n5. Outperformed state-of-the-art explainable and prototype-based classifiers on all three datasets.\n6. Conducted a human study that showed that PCNN explanations improved users' decision-making accuracy compared to showing only top-1 class examples.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Some high-level questions:\n\n1. How does the proposed method perform on tasks other than fine-grained image classification, such as object detection or semantic segmentation (the paper is 47 pages long so hopefully I didn’t miss any results)?\n2. Can the re-ranking algorithm be extended to handle multi-label classification problems?\n3. How does the performance of the PoE model scale with the number of classes or the size of the dataset?\n4. What is the impact of using different distance metrics or similarity measures for finding nearest neighbours on the performance of the PoE model?\n5. Are there any theoretical insights or principles that can guide the design and training of the image comparator S?\n6. In section 4.3 you say “First, we test using cosine similarity in the pretrained feature space of a CUB-200 classifier” – why do you call CUB-200 a classifier? CUB-200 is mentioned 80+ across the paper.\n\nPotential Changes:\n\n1. Explore techniques to reduce the computational overhead introduced by the re-ranking process, such as efficient nearest neighbour search or pruning strategies.\n2. Extend the human study to other datasets and domains to further evaluate the interpretability and usefulness of PCNN explanations.\n3. Provide a theoretical analysis or insights into the effectiveness of the proposed approach, potentially drawing connections to existing theories or principles in machine learning or cognitive science.\n\n\nLastly, it would be greatly appreciated if you have another go to fix typos and some unconventional syntax.\nAlso thanks for providing the source code.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The manuscript proposes a method based on Nearest Neighbor Explanations for top-K predicted classes to improve human decision making accuracy as well as the model's accuracy. Experiments as well as a human study demonstrate that the method is effectie on several fine-grained image classification datasets.
Decision: Accept (poster)