Reproducibility study of “LICO: Explainable Models with Language-Image Consistency"
Authors: Luan Fletcher
Robert van der Klis
Martin Sedláček
Stefan Vasilev
Christos Athanasiadis
Venue: Accepted by TMLR ()

Abstract: The growing reproducibility crisis in machine learning has brought forward a need for careful examination of research findings. This paper investigates the claims made by Lei et al. (2023) regarding their proposed method, LICO, for enhancing post-hoc interpretability techniques and improving image classification performance. LICO leverages natural language supervision from a vision-language model to enrich feature representations and guide the learning process. We conduct a comprehensive reproducibility study, employing (Wide) ResNets and established interpretability methods like Grad-CAM and RISE. We were mostly unable to reproduce the authors' results. In particular, we did not find that LICO consistently led to improved classification performance or improvements in quantitative and qualitative measures of interpretability. Thus, our findings highlight the importance of rigorous evaluation and transparent reporting in interpretability research.

Reproducibility study of "LICO:
Explainable Models with
Language-Image Consistency"
Luan Fletcher*
huan.fietcherr@sttueee....www
Deportment of Computer Science University of Amsterdam
Robert van der Klis*
robert. van. dee..kkis@suudern....m
Department Computer Science University of Amsterdam
Martin Sedláček"
mardin.sclllcckkBssddddd.....u
Department of Computer Science University of Amsteriam
Stefan Vasilev*
stefan, wtt.:r/wwwslleessseee........ uvo.
Department of Computer Science Universily of Amsterdam
Christos Athanasiadis
a.thhoaasiadis Cerisiidddd@uvv.....
Department of Computer Science University of Amsterdam
https: // github. com/Tobertdudk/ fact-fact
Abstract
The growing reproducibility crisis in machine learning has brought forward need for care- ful examination of research findings. This paper investigates the claims made by Lei et al. (2023) regarding their proposed method, LICO, for enhancing post-hoc interpretabil- ity techniques and improving image classification performance. LICO leverages natural language supervision from vision-language model to enrich feature representations and guide the learning process. We conduct a comprehensive reproducibility study, employing (Wide) ResNets and established interpretability methods like Grad-CAM and RISE. We were mostly unable to reproduce the authors' results. In particular, we did not find that LICO consistently led improved classification performance or improvements in quantita- live and qualitative measures of interpretability: Thus, our findings highlight the importance of rigorous evaluation and transparent reporting in interpretability research.
1 Introduction
necessilating the replica- and validation of rescarch findings. Our focus in this paper on reproducing the main results and investigating claims from the work of Lei et al. (2023) regarding their proposed LICO method.
Machine learning facing a reproducibility crisis (Kapoor & Narayanan 2022
LICO aims to improve model interpretability: in explaining model decisions. Especially in high-stakes industries such as healthcare, poor interpretability is a major blocker for wider adoption of deep learning methods ÇZhang et al. 2020: Piccialli et al. 20221).
active area of research, mainly because of its applicability
Equal contribution

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To better understand where LICO positions itself in this domain, can first subdivide model interpretabil- ity approaches into two categories: active and passive (Zhang et al. 2020j. Aclive methods either modify the architecture (i.e. model-based approaches (Murdoch et al. 2019 or the training procedure (e.g. LICO) to achieve better interpretability, while passive or post-hoc approaches (used interchangeably) explain pre- dictions of already trained models without modifications. Post-hoc methods assume that the Teatures of the learned model are non-interpretable. By contrast, for model-based approaches the model itself or its parts can intrinsically help explain the prediction (e.g. weights in linear regression). We can further subdivide post-hoc interpretability methods into two main approaches gradient- bww.propagationn-beeeeeee based (Smilkov ct al. 2017: Sundarara et al. 2017), where explainable noisy gradients are generated, and class activation mapping (CAM) based (Selvaraju et al. 2016 Chattopadhay et al. 2018 Wang et al., 2020), which produce saliency maps. Within this categorisation, LICO is seen as an active method thtt modifies the model training procedure leading to improved post-hoc class activation maps. LICO does not modify model architecture, unlike active modcl-basod methods, making generally compatible with current approaches in deep Icarning according to Lei el al. (2023).
Ordinarily, CAMs are generated using one-hot class labels to determine which pixels in the input image are important for the classification. However using onc-hot encoded labels loads to biased and poor-quality latent representations (Vyas et al. 2020), because one-hot labels carry minimal semantic information. The insight provided by [ei et al. (2023 is that training a network for classification while using natural language supervision from a vision-language model such as CLIP (Radford et al. 2021 to regularise the feature maps may lead more interpretable feature maps, as well as better classification performance. LICO aims achieve this by shaping the latent feature space to approximate semantically rich text encoder space via learnable prompts (Lii ct al. 2023
To investigate the claims about LICO from the work of Lei et al. comparisons between ResNets/ Wide ResNets (He et al. 2015) without the modified training objective from LICO for a subset of the interpretability methods used in the original experiment namely GradCAM (Selvaraju et al., 2016), GradCAM++ (ohadhopyy el al., 2018), and RISE (Petsiuk al. 2018). We also measure classification accuracy for some of the datasets used in the original sctup CIFAR-10, CIFAR-100 (Krizhevsky et al. 2009), and subsets of ImageNet (Deng
2023). we make quantitative and qualitative ZZgoouyko & Komodakis 2017) with and
et al. 2009 Howard 2019 to investigate the claims of improved classification performance.
In the remainder of our work, we first provide more detailed outline of the specific claims made by Lei et al. (2023) about LICO (Section 2}. Next, we explain how we investigated these claims (Section 3 and provide the results of our experiments Section 4 Finally, in Section 5 we discuss the claims of the paper
in the context of these results, and how reproducible the authors' results were.
2 Scope of reproducibility
The LICO method is claimed by Lei et al. (2023) to be qualitative and quantitative enhancement of existing interpretability methods in deep learning. From their experiments and conclusions, we identified the following main claims of the paper:
Training an image classifier with LICO consistently improves quantitative and qualitative model interpretability.
2. Training an image classifier with LICO consistently improves classification accuracy.
3. Tasks with more classification categories benefit more from LICO because the prompt guidance comes from a manifold consisting of more classes
Ultimately, this report aims to investigate the reproducibility of the LICO paper by attempting to fully or partially reproduce its main experiments and determine how strongly they back the aforementioned claims. In Section g we provide more detail on how the original authors set up their method and experiments and compare their results directly with ours in Section
2

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3 Methodology
There are two training objectives added by LICO: firstly, the image manifold is encouraged to have similar shape as the text prompt manifold through the manifold matching (MM) loss, and secondly, prompt tokens are mapped to specific feature maps and vice versa through the optimal transport (OT) loss. The MM loss coarsely aligus the manifold structure, whereas the OT loss establishes tieeggriied relations between individual tokens and feature maps.
As starting point for our implementation, used the publicly available code by the authors Lei et al. (2023). As their codebase incomplete, implemented the training loop, the MM loss and the Inser- tion/ Deletion metrics ourselves (see Section 3.4 Additionally, we made use of existing implementations of
CA based interpretation methods (Gildenblat & contributors 2021 and RISE [Ishikawa 2019 to produce
saliency maps.
(a) Conventional image feature classification pipeline
Image features
Image feature extractor
Image
Classifler
Cross-entropy loss
Text Encoder
Dw
Projecting to common dlimension image features
Text features
Learnable
Text manifold
Image manifold
prompts
(b) Text feature extraction
(c) Coarse Manifold Matching and Optimal Transport Alignment
Trainable
Frozen module
L OT
Optimal Transport
LMM
Manifold Matching loss
module
loss
Figure 1: The pipeline of LICO Lei al. (2023) (a) A regular image classification neural network pipeline, (b) Extraction of language features from learnable prompts and frozen pre-trained text encoder. (c) A visualisation of the two objectives proposed in LICO: manifold matching and feature alignment by optimal transport.
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3.1 Model descriptions
LICO consists of a vision-language model (VLM) with an image encoder, whose embedding space is structured by a pre-trained text encoder that has learned a robust semantic space. This "structuring" is achieved by adding the MM loss and the OT loss terms to the main cross-entropy loss objective.
The outputs the image encoder F E RRx'dd are N feature map embeddings of dimension d' (Figure laj. The inputs the text encoder t, = [X1, [2,,x2,...... XI.... are learnable prompt context tokens Xm concatenated with 4, where I; is the text from the class label (Figure lbp. Each class has its own prompt imput t, with i E {1....K) K), where K is the number of classes. The output of the text encoder for given & is G; RMddd Since the dimensions of the image feature maps and the text features d' and d are of different sizes, the authors use an cxtra mapping multi-layer perceptron hy [d, d"] to map the text features to the dimension of the image features, as seen in Figure 1b]
The MM loss aims to match the manifolds of image features and text features of corresponding samples in batch in embedding space by treating them as two multivariate distributions and minimizing KL diver- gence between them. To transform them to distributions, the authors use the matrices A BBB and A BxB corresponding to G' and F, defined as
A exp (xp(-((EEE)/)
AG Dxp(DD(G.G)/1
(1)
sa exp (-)
ΣS exp (- G.)
where D(...) is a distance metric, like Euclidean distance, and i, index specific feature map embeddings within training batch of size B. Here, Lei al. (2023) state that learnable parameter. We will cover the implications of this design choice in Section 3.3
While the MM loss provides coarse alignment of the structure of the feature maps and encoded prompts, the OT loss aims to match individual feature maps in F; to individual tokens in G; by solving the optimal transport problem (Monge 1781), thus providing a fine-grre grained mapping between the two sets of embeddings. Since the problem intractable in continuous space, it is discretized through the Sinkhorn algorithm (Cuturi 2013). To transform the image and text. features F,, G2 to discrete distributions, Lei et al.] (2023) first perform L2 normalization over the fcature dimension. Then, the normalized fcature space divided into N and M bins respectively via Dirac functions placed al the centre of each bin, which is the discretization step. Formally, a and are the discretized distributions corresponding to F; and G₁:
11
U.
6
(2)
where 6f, and 6gm. are the Dirac delta functions placed at the centre of each bin and un and U. are weights in the probability simplex.
In practice, these are set to uniform weights. Then, via the Sinkhorn algorithm, which speeds up the com- putation of the optimal transport problem by orders of magnitude, as compared to OT solvers, a transport matrix T and cost matrix C are obtained. These are multiplied element wise and summed to get the total optimal transport cost (OT loss):
Lor 1 1 T8)1
The MM loss is coarse because it aggregates the set of embeddings of each input sample, be it image or text, to single vector representation, transforms it to distribution and then uses this per-sample representation to calculate a KL divergence with other sample representations. The optimal transport objective, on the other hand, is argued to be fine-grained because it does not. aggregate the feature maps of a sample to a single representation vector but transforms each feature embedding in F, and G; into respective discrete distributions, whose respective bins represent single embeddings in F, and G..
The total loss of LICO is then L = LCE + aLMM + BLoT.
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3.2 Datasets
We evaluate LICO's classification performance and measure Insertion /Deletion scores (Pessiuk el al. 2018 Zhang et al. 2021 Wang et al., 2020), as defined in Section 3.4 on CIFAR-10, 10, CIFAR-100 100 (Krizhevsky ct al., 2009), Imagenct (Deng et al., 2009), and Imagenette (Howard 2019) datasets. The CIFAR datascts are well-established in literature, while Imagenette is a subset of 10 classes of the ubiquitous ImageNet dataset with 160x 160 pixel images. We turn to this alternative because the images in are larger, like Imagenet, which might mean that they stand to benefit more from LICO in the interpretability evaluation due to the saliency maps being more detailed. At the same time, running Imagenctte instead of Imagenet keeps the amount of compute necessary for multiple training runs manageable. To investigate the results of LICO on the original datasct that the authors used, we also complete single training run on Imagenet with all 1000 classes but 20% of the data.
Even though the authors use CLEAR-10/100 only to measure classification performance, we perform further rnsertionDDelction
investigation and additionally evaluate Insertion DDelction scores. For both datasets, we split the datasct into 47,500 training, 2,500 validation and 10,000 test. samples. On CIFAR-100, we also create a subset consisting of only 2,500 examples to compare the performance of baseline model vs. model trained with LICO when training with limited data.
For Imagenetle, we assess both classification accuracy and Insertion/Delees DDeletio scores (Section 3.4 The dataset is divided into 9000 training samples, 475 validation samples, and 3925 test samples.
During training, we use two augmentations: random horizontal flip and random crop of size H W with a reflect padding of 4, where H and W are the image height and width, respectively. For both training and test sets, we also perform normalization with per-channel mean/std statistics.
We also evaluate the LICO model on the PartImageNet dataset (He et al. 2022). This dataset has seg- mentation masks so that we can evaluate the Intersection over Union (IoU) of the generated saliency maps with the segmentation masks. This provides us with another metric of saliency map quality, as a high score means that the network consistently looking at exclusively the salient parts of images. Since these are ImageNet images, we use the standard ImageNet augmentations: random horizontal flip, resize the short side to 224, random crop, and normalize with per-channel mean/std statistics. In the evaluation phase, we skip the horizontal flip and exchange the random crop for a center crop.
3.3 Hyperparameters
In our reproduction we mainly aim to adhere to the implementation details of the LICO paper. For more details, readers are referred to Section in the Appendix. The Wide ResNet we use for training on CIFAR- 10 has depth factor 28 and wideming factor of 2, as this was also used by Zhang et al. (0022) Sohn et al. (2020g, and the authors of LICO used many of the experimental scttings from those papers. On CIFAR-100 we use a depth of 28 and a widening factor & for the same reason, and on Imagenette we use the same settings as on CIFAR- 100. The number of context tokens in a prompt set to 12, as the ablation study from Lei et al. 2023) shows that the LICO losses give the best results with 12 context tokens.
We also take closer look into the authors' design choice of 7 (Equation I being learnable parameter. It should be noted that as increases to arbitrarily large values, both Ar, and AG approach constant value:
exp(-D(a,b)/1
exp(O)
1
lim B E (oo0-D(a,bb//1
Σ exp(O)
Thus, in the limit, both A and A will be matrices with all entries equal to 1. This, in turn, means that
B L 1=1
fMM B
0.
Thus, it scems likely that 7 should either not be Icarnable, or if it is learnable, ought to be clipped to a maximum value. We perform experiments with it being either a learnable parameter or a constant 7 1 to determine whether this matters in practice.
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3.4 Experimental setup and code
In this study, we ran experiments aiming to reproduce the main parts of the LICO paper and its results. Our code and hyperparameters follow exactly the methodology and values described in the paper, but we run two experiments beyond the original paper: one with the aim to investigate the generalisability of the authors' claims about model interpretability by evaluating the loU between ground-truth segnentation masks and saliency maps. With the other auxiliary experiment, we aimed to investigate whether the T parameter should be a learnable parameter or a constant.
As image encoders, we use Wide ResNet for the CIFAR-10, CIFAR-100, and Imagenette datasets, and we use a ResNet18 (He et al. 2015 for the Imagenet and PartImageNet datasets.
Three metrics used to mcasure the performance of LICO against non-LICO trained model classifi- cation accuracy, Snneertoonnelletii scores, and the IoU between generated saliency maps and ground-truth segmentation masks. The Insertion score is determined by starting from a blurred input image, and gradually unblurring pixels, where the order in which pixels are unblurred is determined by the saliency map. Then, if the saliency map has correctly assessed which pixels are important for the network to make its prediction on the partially blurred image, the class probability should quickly rise as a function of unblurred pixels. The Insertion score the AUC of this curve, so a good Insortion score close to The Delction score is mea- sured analogously, by starting with the base image, and gradually removing pixels; a good Deletion score is close to 0. We measure Insertion/ DDeitiion in conjunction with interpretation methods - Grad-CAM. Grad-CAM+ and RISE. This follows the same sctup as the original experiment conducted by Lei et al. (2023). We also show an Overall score (i.e. insertion deletion) in conjunction with the Insertion/ Deletion like the authors of LICO.
DeeeetionDDeletinn
As for the IoU scores, they serve to evaluate whether the model accurately focuses on relevant arcas within the image. high IoU indicates consistent attention to the object of interest rather than the background. To incorporate ToU into our analysis, we must interpret it within the context of non-binary saliency maps and binary ground truth labels regions for comparison. We utilize the original loU formula, which is sensible because we can view the saliency map region as soft assignment of a region for segmentation. This approach offers an alternative metric for assessing saliency map quality, leveraging segmentation masks as a potentially stronger signal than class probabilities in insertion/ deletion that since segmentation maps directly highlight the object of classification, they serve as an effective proxy for evaluating saliency map quality.
/dsettoon/eeletio scores. Furthermore, we hypothesize
Lastly, code is available GitHul
4 Results
We have performed extensive experimentation with the aim of testing the claims of the authors. It includes testing the interpretability claims and the classification accuracy improvement claims of the authors. We further show our results on an ablation study on the different loss terms introduced and on our extensions of the paper's experiments - comparing segmentation labels with our saliency maps and the effects of a learnable parameter.
Overall, our results do not support the central claims of the paper. Saliency map interpretability did not increase either quantitatively qualitatively for models trained using T.COO. Tn addition, the use of LICO decreased classification performance across the board. The results of our extension are also in lin with these findings.
In the following subsections, we will analyze the results of each experiment in detail and provide evidence for our discussion in the following section.
http:///gthuua.cc//rrbbrrtdd///d
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4.1
Claim 1: Improvement in interpretability
Table 1: Baseline /LICO Insertion, Deletion, and Overall (Insertion Deletion) scores for CIFAR-10, CIFAR-
100; Imagenette, and ImageNet (20%). Numbers in bold are the highest. for a specific interpretability method.
Dataset
Method
Grad-CAM+
Grad-CAM
RISE
CIFAR-10
Insertion
53.8/48.7
63.0/63.
73.3/72,4
Deletion
43.8/ /4.9
37.3/35.4
31.8/30.0
Overall
9.9/1.7
25.8/28.2
41.5/42.
CIFAR-100
Insertion
34.1/33.8
44.0/43.4
54.3/54.2
Deletion
27.6/28.6
19.2/19.8
13.4/14.3
Overall
6.5/4.2
26.2/23.6
40.9/39.9
Imagenette
Insertion
60.2/59.2
77.1/74.5
70.9/70.3
Deletion
36.9/ /38.3
23.0/25.5
36.2/35.8
Overall
23.3/21.0
54.1/490 49.0
34.7 /3.5/34.5
ImageNel
Insertion
29.9/23.8
32.6/24.7
Deletion
7.5/5.0
5.3 4.4
Overall
22.4/18.8
97.3/20.4
As shown in the results in Table did not replicate the authors' findings that LICO generally improves the interpretability of saliency maps. With the exception of CIFAR-10 using Grad-CAM/RISF,, LICO reduced our quantitative measure of interpretability Inssertion/Deletinnn scores) for every saliency map method and dataset tested.
Input
Bascline
LICO
Input
Bascline
LICO
Figure 2: Examples of GradCAM saliency maps for a model trained with and without LICO. Both models were trained on the same 20% subset of ImageNet. On the left, we the validation set images that the LICO authors used, while on the right see images from the test set, picked by us.
As for the qualitative side of measuring interpretability, Figure also demonstrates that the generated saliency maps do not show any obvious qualitative improvement when training with LICO, counter to the authors' claims. Furthermore, we argue that LICO does not visibly produce more explainable attention maps than the baseline: neither in our saliency maps, nor in those of the original paper.
On the left Figure P we see the original images from the validation set that the authors use. We get different saliency maps than the authors, possible because we have fewer images per class. Apart from that, note that LICO's saliency maps to be focused slightly less important regions of the images than
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the baseline. This hints towards worse generalization ability for dataset with many classes and few images per class. We show in Section 4.3. that, worse saliency maps also translate to lower accuracy than baseline.
4.2 Claims 2 and 3: Improvement in classification accuracy
We find that LICO slightly decreases test accuracy on all four datasets (Table 20, as well the smaller subset of CIFAR-100. These results also contradict the authors' claim that greater number of classification categories leads to a greater increase in classification performance when using LICO: we find that LICO does not perform any better in the ImageNet and CIFAR-100 cases with 1000 and 100 classes respectively, than in the CIFAR-10 and Imagenctte cases with 10 classes.
Similarly, our results also contradict the authors' assertion that LICO is particularly beneficial in circum- stances with limited data. We instead find that in the limited data case, the degradation in performance due to LICO is significantly worse than in the full data case. It should be noted that, in general, our results are much worse than the authors' results in this case. This leads us to believe that the authors did something that was not described in the paper to improve results on limited data. This missing part might also explain why, in our case, LICO was much worse with limited data, whereas in their case, LICO was better.
Table 2: Test accuracy of baseline models against LICO models on ImageNet, Imagenette, and CIFAR10/100. The ImageNet model was trained once, the other models were trained three times with different seeds. The ImageNet baseline model was ResNet-50, and for every other dataset we used Wide ResNet-18. A limited case with 2500 examples for CIFAR-100 is also evaluated.
ImageNet
Imagenette
CIFAR-10
CIFAR-100
CIFAR-100 (2500 examples)
Baseline +LICO
49.75
87.3 + 0.74 93.9 0.13
78.1 = 0.66
28.5 0.39
48.18
86.8 + 1.58
93.6 + 0.29
77.3 + 0.90
18.6 + 1.64
4.3
Loss ablation study
As further investigation into why most of our results differ from the authors, we performed an ablation study on the individual loss components. Specifically, we trained models on CIFAR-10, CIFAR-100 and Imagenette using only cross entropy loss and one of the OT/MM losses.
4.3.1 Classification performance
Table 3: Test accuracy of LICO with ablated losses (over three runs) on CIFAR-10/100, Imagenette and CIFAR-100 in the limited data setting.
Imagenette
CIFAR-10
CIFAR-100
CIFAR-100 (2500 examples)
Baseline
87.3 + 0.74
93.9 0.13
77.7 0.08
28.5 + 0.39
OT only
83.8 + 1.58
93.6 + 0.35
78.0 + 0.38
23.6 + 1.96
MM only
88.0 + 0.26
93.9 0.11
78.6 0.33
26.1 + 1.10
LICO (OT MM)
86.8 + 1.58
93.6 = 0.29
77.0 + 1.08
18.6 = 1.64
In the original paper, the authors perform one ablation study on the individual losses only for the Imagenet dataset. It shows that using only the OT loss leads a degradation in test accuracy versus baseline and using only the manifold loss leads to improvement in test accuracy versus baseline. We find that training with the OT loss alone leads to small drop or no change in test accuracy while training with the manifold loss alone leads to a small increase or no change in test accuracy, approximately replicating the authors' results (Table 3
The one exception to our findings is in the case of limited data, where the baseline model remains significantly better than all other models. Even here, however, the model trained using only the manifold loss performs the best of the three non-baseline models.
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The authors also find that using both losses leads to greater improvement in test accuracy than using the manifold loss alone. We do not see this in our results. Instead, we find that using both losses is always worse than using the manifold loss alone.
4.3.2 Interpretability
In the authors' ablation study, both the OT loss by itself and the manifold loss by itself improve the interpretability of saliency maps as measured by Overall scores, and they find that the best performance this metric is achieved with the use of both losses. Our results do not bear this out, with the baseline models more often than not performing the best (Table 4 We also do not providing more improvement than another, with all of the non- ban--aseline models performing approximately equally when averaged over the different datasets and CAM methods.
clear pattern of loss
Table 4:
Ablation study results of LICO with MM/OT losses evaluated for interpretability: Insertion, Deletion and Overall scores. The results are on CIFAR10/100 and Imagenette datasets.
GradCAM
GradCAM++
RISE
Dalaset
Method
Ins
Del
Ovr
Ins
Del
OVI
Ins
Del
Ovr
Baseline
63.0
37.3
25.4
53.8
43.8
9.9
73.3
31.8
41.5
CIFAR10
MM only
59.3
39.5
19.8
51.5
46.4
2.6
72.8
30.9
41.9
OT only
63.0
36.8
26.2
53.5
44.4
9.1
73.9
30.4
43.5
LICO (OT + MM)
63.6
35.4
28.2
48.7
46.9
1.7
72.4
30.0
42.4
Baseline
44.0
19.2
26.2
34.1
27.6
6.5
54.3
13.4
40.9
CIFARI00
MM only
41.0
21.2
19.8
31.8
29.2
2.6
55.0
13.3
41.7
OT only
41.3
20.8
20.5
32.9
27.5
5.4
55.1
13.3
41.8
LICO (OT + MM)
43.4
19.8
23.6
32.8
28.6
4.2
54.2
14.3
39.9
Baseline
77.1
23.0
54.1
60.2
36.9
23.3
70.9
36.2
34.7
Imagenette
MM only
75.9
27.0
48.9
60.4
39.0
21.4
69.1
38.0
31.1
OT only
71.7
27.3
44.4
57.3
38.3
19.0
64.7
37.8
26.9
LICO (OT MM)
74.5
25.5
49.0
59.2
38.3
21.0
70.3
35.8
34.5
4.4
Results beyond original paper
4.4.1 Saliency maps and segmentation ground truths comparison
We perform a further investigation of the model's interpretability by evaluating the loU between ground truth sogmentation masks and saliency maps generated with Grad-CAM (). Over three runs, we get mean IoU of 0.185 for the baseline model against. 0.141 for LICO, which is 23.7% lower. This is a substantial difference, suggesting that LICO produces worse-quality saliency maps because focuses less on the segmented object of classification and is possibly more spread out. As a visual example, in Figure 2 Example 2 we see that the saliency magnitude is low even though it is focused the correct object, which would result in low ToU for this image.
Method
Intersoction over Union
Baseline
0.185 + 0.005 0.141 + 0.008
LICO
Table 5: Intersection over Union scores, evaluated on PartImageNet

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4.4.2 The effect of learnable versus constant 7
To investigate the effect of using fixed learned lemperature, we performed an experiment on CIFAR-10 where we compared 3 models: one without LICO losses, one with LICO losses but with fixed temperature, and one with LICO losses and with learned temperature.
GradCAM
GradCAM+
RISE
Method
Accuracy
Ins
Del
Ovr
Ins
Del
Ovr
Ins
Del
Ovr
Baseline
93.6 + 0.09
63.9
38.0
25.8
56.0
44.1
11.8
69.7
34.0
35.7
LICO (T learnable)
93.5 = 0.08
66.8
36.6
30.1
50.1
47.9
2.2
68.5
34.5
34.0
LICO (T fixod)
93.4 + 0.08
65.2
36.5
28.7
49.2
49.2
0.0
67.7
36.2
31.4
Table 6: Test accuracy, Insertion/Deletion and Overall scores for the experiment with for LICO, The setups are LICO with learnable 7 against fixed with a baseline no LICO training.
In Table D we see that keeping r fixed does not seem to have any beneficial effect. The test accuracy is not significantly different. from the test accuracy with a learnable and the Insertion /Deliiiin scores are generally worse than with a learnable т. One possible reason for the lack significant differences in our results that the LICO losses do not provide any benefit. Consoquently, parameters that adjust how the LICO losses work might have no effect if the LICO losses themselves are ineffective.
Our hypothesis with this experiment was that keeping fixed would prevent the manifold loss from vanishing during training, therefore positively influencing the training process. However, it does not have a significant effect across datasets. Therefore, the design choice of r being learnable is unlikely to matter for the results obtained from LICO.
5 Discussion
In general, our results did not support the claims of the paper. We extensively tested the methodology of the paper across CTFAR-10 and CIFAR-100 datasets, because these datasets were manageable in size while also having results in the paper could compare to. We performed many iterations and variations of experiments in effort to replicate the reported results. However, in all our experiments, we did not observe consistent improvements in accuracy, nor did we detect qualitative or quantitative enbancements in interpretability.
We did replicate some of the findings of the authors regarding ablation on losses. Specifically, we found that using the manifold-matching loss alone leads to consistently better classification performance than using the OT loss alone. However, did not replicate the finding that using both losses performs best. This implies that much of the difficulty in replicating the authors' findings is due to issues with the OT loss. It is possible that our implementation of the OT loss differs substantially from that of the authors, but we are unable to confirm this due to incomplete code provided by them and a lack of response to our inquiries seeking clarification. We started experimenting with the OT loss code provided by the authors in their repository, but this, too, did not yield the expected results reported by Lei al. (2023) in the original paper. Finally, we attempted to implement the OT loss oursclves from scratch, exactly as is described in the LICO paper. This implementation also failed to reproduce the reported results despite this being the intended implementation of the OT loss, based on its description by Lei et al. (2023) (see section 5.2 for further details).
A limitation of our study is that our implementation may differ substantially from the implementation the authors used to generate their results (see Section 5.2). It likely that we missed several setup details and optimizations that the authors used to get the networks to train slightly botter, resulting slightly better accuracy. Thus, all we can conclude is that the paper is not reprodueible as it is now, given the existing resources: the paper itself, and the public code repository.
10

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5.1
What was easy
The paper gives both an intuitive as well as a formal description of the losses, and pseudocode for the training algorithm, which is very helpful in understanding how the method works and why. Thus, the core contributions of the paper are well explained.
In addition, the paper provides extensive experimentation and results with discussions to solidify the scope of the proposed methods and to evaluate them from different aspects. We found this very helpful for understanding why and to what extent LICO performs well.
5.2 What was difficult
Attempting to reproduce the LICO paper was, in general, quite difficult. This caused by three main factors: firstly, the code provided by the authors was incomplete. The MM loss function and the training loop were missing, as well as the implementations for a ResNet50 (He et al., 2015 and PARN (He et al, 2016) with prompts. We attempt to alleviate this problem by providing our own codebase, where many parts the methodology are implemented exactly as described by Lei et al. (2023).
Secondly, in several cases, the provided code did not match the methodology as described in the paper. Most notably, the paper proposed "to align G; (...) and F; (...) for achieving consistency between feature maps and specific prompt tokens". However, the implementation in the code provided by Lei et al (2023) reduces the output of the text encoder into a single feature vector per class instead. This does not correspond to the description in the paper, and based on our observations, this discrepancy renders the OT loss meaningless. This reduction to a single vector in the authors' implementation is realized via an '.' (EOT) token contained in the corresponding prompt G; for every class, which being used like a [CLS] token in ViT (Dosovitskiy ct al. 2021). ) Additionally, the paper, the learned context is shared between the prompts for every class: in Gū, the only part that depends on the sample index gr.- In the code, there is separate learned context for every class prompt.
Finally, details of the experimental setup were ambignous, which exacerbated the difficulty of reproduction. The paper did not describe the depth and width of the WideResNet used to generate their results on CIFAR- 10, CIFAR-100, and SVHN. Instead, they referred to another paper's setup without pointing out what part of the experimental setup was used. The paper also missed a justification for a key design choice: in Equation 7 is defined to be learnable parameter, which leads to issues the MM loss (see Section 3.3 discussion of the implications). This prompted our investigation in Section 4.4.2
5.3 Communication with original authors
We attempted to establish communication with the authors persistently over a span of more than four weeks. Regrettably, we received no response to any emails. This substantially hindered the efforts to reproduco the original experiments, as the code provided was incomplete and inconsistent with the paper itself on multiple occasions (see Section 5.2 for more detail). We were only provided the code in this state after emailing the authors about the lack of code in the paper's repository. This was the only time received any response to our queries, although the emails themselves never received an explicit reply.
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13

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A Hyperparameters
All typerparameters are shown in Table 7
We keep a = and 3 = for experiments. The batch size kept at for setups other than ImageNet, and the learning rate at 0.03. We use SGD as optimizer with momentum 0.9 and weight decay 0.0001.
We apply the cosine learning rate scheduler 7= no cos CIFAR-10/100 for 200 epochs and on ImageNet and Imagenette for 90 epochs.
Inn from the paper Lei et al. 2023 and train 16k
Hyperparameter
Value
a
Batch size
64 and 128 (ImageNet)
Learning rate
0.03
Optimizer
SGD
Momentum
0.9
Weight decay
0.0001
Learning rate scheduler
Cosine
Learning rale scheduler parameters
= 1o cos
16K
Tmn
Training epochs (CTFAR-10/100)
200
Training epochs (Imagenette, ImageNet)
90
Table Hyperparameters for experiments
B Computational requirements and environmental impact
For our runs we used NVIDIA A100 GPUs on a cluster. Completing a single training run on CIFAR-10 with and without LICO took around 2.5 hours, on CIFAR-100 around 4 hours, on Imagenette approximately 1.5 hours, and on PartemageNNe around 2 hours.
To calculate the environmental impact of our reproducibility study, we use the following formula:
CO2e CT PUE E,
where CI Carbon Intensity (g CO2e/kWh), PUE is the Power Usage Effectiveness (no unit), and E is the energy used (kWh). For the CI, we the most recent value provided by the European Environment Agency: in the Netherlands in 2022, generating one kWh of electricity emitted an amount of greenhouse gases equivalent to 321 grams of CO2 As for the PUE, we do not know the value of Sncllius specifically, but the European Commission has stated that the average value in the EU is 1.6 so that is the value we will use. In our efforts to reproduce the paper, we used an estimated 205 kWh (calculated using the "eacct" command of the "Energy Aware Runtime (EAR) SLURM plug-in'). Putting this all together, we arrive total of
321g CO2e/kWn 1.6 205kWh 105 kg COge.
If we compare this the greenhouse gas emissions per capita, which was 9600 kg CO2e per year in the Netherlands in 2021 [ find that running the experiments for paper used approx. 11% of what Dutch citizen uses per year. Alternatively, comparing lo the EU CO2 emission larget for passenger cars, which is 95 g CO,/km, our study used the same amount of CO2e as driving a passenger car for approx. 1105 km.
14

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C Hyperparameter search
In order to investigate the impact of the hyperparameters on performance, we trained a model on CIFAR-100 with varying typerparameters and a set seed. Below we report these results. The best results in each table are bolded.
C.1 Weight of manifold and OT losses
Table 8: CIFAR-100 test accuracy by manifold loss weight (a) and OT loss weight (8).
B
1
5
10
77.9
77.6
76.5
75.4
77.6
1
78.7
78.2
76.3
76.0
77.0*
a
2
78.2
77.8
77.2
76.6
75.4
77.8
77.8
77.4
76.6
76.5
78.7
77.0
76.1
76.8
74.4
There is a general trend of decreasing performance as the weight. of the OT loss is increased. Further, the best performance is achieved when the OT loss not used at all (i.c. when B = =).
C.2
Weight decay
Table 9: CIFAR-100 test accuracy by weight decay.
Weight Docay 10
10-3
10
10
10 6
Val Accuracy
19.7
73.8
77.0"
76.7
76.0
The weight decay value used in the original paper appears to lead to the best performance.
C.3 Number of epochs trained
Table 10: CIFAR-100 accuracy by numbers of epochs trained.
Number of Epochs
200
300
500
Test Accuracy
77.0*
77.0
76.3
There does not appear to be any improvement in accuracy when increasing the number of epochs trained for.
These are the hyperparameters used in the LICO paper
15


Reviews:
[{"review_id": "xenszUQD57", "reviewer": "Reviewer_A9LE", "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[+] Reasonable reproducibility study:\n\nThe authors have done a good job at summarizing the findings in LICO, defining an experimental setup to test these hypothesis, and showing that in their setup they were not able to reproduce the results in the original paper,\n\n**Weaknesses:**\n\n[-] The scope of the paper is quite limited:\n\nThe paper limits itself to reproducing another method (unsuccessfully). As such, it only provides evidence towards invalidating the claims in LICO. The study adds a small experiment checking the IoU of saliency and segmentation maps for a baseline and a model trained with LICO, but does little to go beyond the original paper and to provide insights as to why the study was unsuccessful.\n\n[-] The experimental setup is limited compared to the original LICO paper.\n\nWhile the authors tested some of the datasets in LICO, they mostly used small datasets (CIFAR-10/0). While it is true that the claims do not hold for such datasets, for thoroughness it would be important to test other setups (SVHN, ImageNet) in the paper.\n\n[-] Errors/typos in the text, copied figures, repeated content.\n\nThe paper is quite unpolished, with a considerable amount of grammatical errors and typos/spelling mistakes that make it difficult to read it. Additionally, Figure 1 in the paper is a copy of Figure 2 in LICO and is not properly attributed. Finally, Section 3 contains mostly content already found in the LICO paper, with some of the formulas copied verbatim.", "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 attempts to reproduce the main findings in the LICO paper. The authors first summarize the main claims in that paper, then describe the method in the original paper as well as the experimental setup for their reproducibility study and, finally, they report and discuss their results. The authors find that none of the claims in LICO holds for their reproducibility study.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "* Conduct the analysis with ImageNet.\n* Rewrite Section 3 with original figures and an interpretation of the original LICO paper, rather than copying content from that paper.\n* Polish the text and provide insights as to why the paper could not be reproduced.", "broader_impact_concerns": "No concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "lUm6xOsLnG", "reviewer": "Reviewer_C77H", "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- The paper is well-structured.\n- The authors clearly highlight how difficult it is to reproduce the LICO paper. This underscores the importance of a thorough peer review process.\n- Experimental results are well presented, where the authors employ diverse datasets (Imagenette (small subset of ImageNet), CIFAR-10, and CIFIAR-100) and baseline algorithms (GradCAM, GradCAM++, and RISE).\n\n### Weaknesses\n- The current manuscript could benefit from experiments with larger datasets such as ImageNet and more complex models such as ResNet-50. Although the current experiments offer valuable insights, it is worth mentioning that that the key comparison on interpretability and classification performance in the original LICO paper were conducted with ImageNet and ResNet-50. Not only will a larger dataset allow the model to capture more precise saliency maps, but a larger model may also be useful for learning substantial information in the CLIP language encoder (VIT-B/32).\n- A more in-depth ablation study of the OT loss function used in LICO is required. It is acknowledged that precisely implementing the OT loss is challenging, as the authors mentioned the Discussion section. However, the paper does not sufficiently show an attempt to implement the OT loss. Interestingly, experiments in Table3 shows the efficacy of the MM loss of LICO.\n- The clarity of the writing could be improved in some sections. Specific suggestions are provided below.", "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": "This paper investigates the reproducibility of LICO [1]. The authors make the following key contributions:    \n\n1. The authors provide a clear review of the methods and claims in LICO.\n2. Since the original LICO repository lacked information necessary for reproducing the original experimental results, the authors have reimplemented the LICO code and made it publicly available.\n3. The authors evaluate the effectiveness of LICO by demonstrating its impact on both interpretability and classification accuracy.    \n\n[1] Yiming Lei et al. LICO: Explainable Models with Language-Image Consistency. NeurIPS 2023.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "No", "audience_explanation": "", "requested_changes": "- [introduction] What is different between model-based approaches and class activation mapping based approaches? Is there no overlap between the two methods? Categorizing methods with paper citations would improve the readability of the paper. \n- [3.2 dataset] Imagenette and ImageNet contain 14k images and over 1M images, respectively. Considering Imagenette as a large dataset may be an overstatement.\n- [4.1 Results] It would be valuable to use the same images in LICO, for the qualitative experiments. The left image in Figure 2 does not provide useful insights since the baseline algorithms (GradGAM) has already poor results.\n- [A Hyperparameters] In LICO, the batch size is 128 for ImageNet experiments. It would be valuable if the Imagenette experiments are also conducted with the same batch size, not 64.\n\nMinors\n- [introduction] It would be more appropriate to place the citation at the correct location. For example, changing `between ResNets and Wide ResNets with and without the LICO losses (He et al., 2015; Zagoruyko & Komodakis, 2017)` into `between ResNets (He et al., 2015; Zagoruyko) and Wide ResNets (Zagoruyko & Komodakis, 2017) with and without the LICO losses`", "broader_impact_concerns": "Please refer to the weaknesses.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "kEptdPCrbO", "reviewer": "Reviewer_SYve", "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": "The author(s) of this paper did a very good job of trying to reproduce the claims made in the original paper.\n\nIn terms of the strengths:\n- Thorough evaluation across multiple datasets and interpretation methods to comprehensively test the original claims (they used CAM-based interpretation methods proposed by Gildenblat et al., 2021 and RISE (Ishikawa, 2019).\n- Implemented additional experiments beyond the original paper, like IoU computation (due to a dataset used that has segmentation masks  available) and ablation on loss terms.\n- Identified issues in the original paper's description and provided code that complements that of the original authors (incomplete hence hindering reproducibility).\n- Transparent reporting of computational requirements and environmental impact (author of the reproducibility paper added an appendix related to this).\n\nWeaknesses:\nI do not think the below-listed weaknesses are necessarily attached to the reproducibility effort but some limitations are:\n\n- Unable to successfully reproduce most of the original paper's findings, especially on improved interpretability and classification accuracy with LICO (lack of source code definitely an issue but it is not clear whether the effort without the source code, i.e. just by replicating the paper, fell short or the details in the original paper were not enough; I am not certain).\n- Had to make several implementation choices due to ambiguities and missing details in the original paper (again similar to point 1 above).\n- Lack of response from original authors to clarify issues that did not help with the reproducibility efforts (especially given that some information on loss functions is missing from the original paper)", "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": "- This paper attempts to reproduce the key claims and experiments from the original LICO paper, which proposed using natural language guidance from vision-language models to improve image classification performance and interpretability.\n- The authors re-implemented the LICO method and losses (manifold matching and optimal transport) and evaluated it on CIFAR-10, CIFAR-100, Imagenette and PartImageNet datasets.\n- They measured classification accuracy as well as quantitative metrics like insertion/deletion scores and IoU with segmentation masks to assess interpretability improvements claimed by the original paper.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Overall I think this is a very decent effort to reproduce LICO.\nI believe the efforts the authors put into trying to reproduce this paper have been reasonable.\nI guess one could argue that more effort to find the missing hyperparameters and prompts could have made the reproducibility more successful.\n\nCould you please articulate how much effort went into trying to do the above? Apologies If I have missed that bit in our paper.", "broader_impact_concerns": "None", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The reviewers unanimously recommend the publication of the paper.  This represents an interesting study of reproducibility.  Beyond the investigation of the reproducibility of the LICO paper, this work will help future papers avoid pitfalls that could impede reproducibility. 
 Hence I recommend a reproducibility certification.
Decision: Accept (poster)