White Paper Assistance: A Step Forward Beyond the Shortcut Learning
Authors: Xuan Cheng
Tianshu Xie
XiaoMin Wang
MingHui Liu
Jiali Deng
Ming Liu
Venue: ICLR 2022 Submitted (2022)
Keywords: Shortcut Learning
Bias
Classification
Imbalanced Classification
Robustness

Abstract: The promising performances of CNNs often overshadow the need to examine whether they are doing in the way we are actually interested. We show through experiments that even over-parameterized models would still solve a dataset by recklessly leveraging spurious correlations, or so-called ``shortcuts’’. To combat with this unintended propensity, we borrow the idea of printer test page and propose a novel approach called White Paper Assistance. Our proposed method is two-fold; (a) we intentionally involves the white paper to detect the extent to which the model has preference for certain characterized patterns and (b) we debias the model by enforcing it to make a random guess on the white paper. We show the consistent accuracy improvements that are manifest in various architectures, datasets and combinations with other techniques. Experiments have also demonstrated the versatility of our approach on imbalanced classification and robustness to corruptions.

Under review as a conference paper at ICLR 2022
WHITE PAPER ASSISTANCE: A STEP FORWARD BE- YOND THE SHORTCUT LEARNING
Anonymous authors Paper under double-blind review
ABSTRACT
The promising performances of CNNs often overshadow the need to examine whether they are doing in the way are actually interested. We show through ex- periments that even over-parameterizee models would still solve a dataset by reck- lessly leveraging spurious correlations, or so-called "shortcuts". To combat with this unintended propensity, we borrow the idea of printer test page and propose a novel approach called White Paper Assistance. Our proposed method is two- fold; (a) we intentionally involves the white paper to detect the extent to which the model has preference for certain characterized patterns and (b) we debias the model by enforcing it to make random guess on the white paper. We show the consistent accuracy improvements that are manifest in various architectures, datasets and combinations with other techniques. Experiments have also demon- strated the versatility of our approach on imbalanced classification and robustness corruptions.
INTRODUCTION
We don't 't see things as they are; we see them as we are.
-An Old Proverb
These words give us insight into the predictable irrationalities of the human mind. Individuals always create their own "subjective reality" from their perception. Psychological researches (Haselton al.) 2015 Zhang et al. 2007; Shafer et al. I 1984 Kahneman & Tversky 1996) term this systematic, irrational, unconscious error that can dramatically alter the way we perceive the world as "cognitive biases". Similarly to the behavior of human, convolutional neural networks may also develop their own biases during training, by learning "shortcuts" (Geirhos et al. (aloo known as "spurious cues" (Hendrycks et al.) 202T) or "superficial correlations" (Jo & Bengio 2017; Pezeshki et al. )020 which perform well on the existing test data but would fail dramatically under more general settings.
There is a large volume of published studies describing and analysis this learning dynamic. this work, adopt the gradient starvation hypothesis, proposed in (des Combes et al., 2018 Pezeshki et al. 2020). that the leading cause for this feature imbalance that the neural network is biased towards capturing statistically dominant features in the data so that it starves the learning of other very informative but less frequent features. With this being considered, a natural question is how to favor generalizable features over shortcuts? It seems that the most reasonable and direct way is to identify which features contain shortcuts (like green to frogs) and which features should be enhanced (like shapes to animals)? Unfortunately. most patterns that CNNs rely on to classify do not appear in form amenable to discover. And enhancing specific features requires specific expert knowledge, let alone extensive manpower and resources.
Luckily, CNNs are not alone with this issue. Very much like the networks submitting to spurious preference, the printer sometimes may use an unintended color to represent the intended color. In the real world, we call it color cast problem. When a colored image is fed into printer, the printer has to perceive it and then duplicate using the right color. The color cast problem thereby indicates the wrong propensity of color using. In practical use, when color cast problems, we usually let the printer print a white paper. Once this white paper is printed into other colors, the color cast is thereby detected and we need seek corresponding solutions.
suspect that our printers are having

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Put another way, the white paper here serves as a prefect indicator of the color cast problem. This common sense motivates us to exploit the use of white paper to regularize the model.
Intuitively, the white paper does not belong to any classes the model have learned from whichever benchmark dataset. A idealized model should thereby give an inference result that is almost as if makes random guess, to demonstrate it does not mistake this sample any class it has learned. Consequently, when discovering a difference between the intended and actual outcome, we could know that the model now has some unintended generalization directions, which should be thought of as a consequence of shortcut learning. Simply put, the white paper could also act like a "test paper" in detecting dominant patterns. The experimental results in Section 3 will prove that the use of the white paper is an effective and universal choice, even when compared with some real datasets.
Leveraging the superior ability of the white paper in detecting dominant patterns, we derive an interesting and effective regularizer called White Paper Assistance, which alleviates the excessive reliance of these dominant features by repeatedly enforcing the model to make a random guess on the white paper. Our method does not require any further supervision the bias, such explicit labels of misleadingly correlated attributes (Kim et al. 2019 [i & Vasconcelos 2019 Sagawa et al. 2019). or ddomainspeciffi--iis-- biis--ailloeed training technique (Wang et al.] 2019 Geirhos et al. [019] Li et al.,
2020). Moreover, despite the simplicity of implementation of the white paper, our method can effectively improve the model's generalization ability and help produce better performance. Since the whole algorithm does not entail any modification on model architectures and any interference to original training, it can be easily assembled into various CNNs as "plug-and-play" components, which significantly promotes its value in practice.
Here summarize our contributions:
We propose novel method called White Paper Assistance to alleviate the shortcut learning. Our method does not require modifying the network and is easily implementable on any modern neural architecture.
We show the superior ability of the white paper in detecting dominant patterns.
We experiment with various architectures, different benchmark datasets. different combi- nations of techniques show the wide applicability and compatibility of our method.
We test method in imbalanced classifications and robustness against corruptions to demonstrate the versatility.
RELATED WORK
With the emergence of deep learning, numerous astonishing stories (He et al. 2016 Chen et al. 2017 Yun et al. 2019, Radosavovic et al,, 2020) about tremendous performances of CNNs have rapidly spread all over the field. However, despite the ever-increasing pace, CNNs share the same vulnerability as the human cognitive system, bias. has been demonstrated that models may learn spurious shorteut correlations, which may be sufficient to solve training task but clearly lack of generalization utility. For example, a model would identify cows in "common" (e.g. pastures) contexts correctly but fail to classify cows in "uncommon" (e.g. beach) contexts (Beery et al.)) Standard ImageNet-trained models prefer to label cat image with elephant skin texture as elephant instead of cat (Geirhos et al. 2019). Such phenomenons (Nguyen et al. 2014] Wichmann et al., 2010 Ribeiro et al. 2016) highly exemplify the contradiction between the shortcut correlations and the intendintended generalization.
As an active line of research, numerous studies have provided different explanations for this phe- nomenon (Nasim et al. 2019; Xu al.) 22199] Parascandoloo et al. 2020). For example, Valle-Perez et al. 2019) suggests that the parameter-function map of networks would bias towards simple func- tions. Kalimeris et al.) justifies the simplicity bias further by showing that SGD learns functions of increasing complexity. Herrmnn & Lampinen (2020) demonstrates the model being "lazy" that it would favor the casier-to-extree feature over a more predictive feature. this paper, we follow the explanation proposed in (des Combes et al. 2018 Pezeshki et al. 2020) and argue that the rationale behind this learning proclivity for shortcuts is the propensity of the model to capture statistically dominant features in the data, rendering failure discovering other predictive features.
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Recently studies that relates to shortcut removal usually requires extra supervision (Kim al. 2019
Sagawa et al. ) Li Vasconcelos (2019) explicitly add color bias as side information to an unbiased dataset of grayscale images. Geirhos et al. 2019)) use style transfer to synthesize data to help generate more preferable shape-haset representation. Li et al. (2020) further provide super- visions from both shape and texture when generating cue conflict images and lead to better feature representations. Instead of leveraging laborious and expensive supervision, our method utilizes the common sense by leveraging the white paper detect the dominant patterns.
OUR METHOD
The algorithm we propose conceptually simple and plug-and-pll method that can be easily integrated into various CNN models without changing the learning strategy. The pseudo-code of the White Paper Assistance is shown in Algorithm I Generally speaking, the aim of this algorithm is to detect and conquer.
For certain epoch from training iteration, the probability to conduct White Paper Assistance is P. and - P if otherwise. Once applying it, batch of white paper will be fed into the model and we can obtain the normalized output distribution (using "softmax") The distribution here represents the perception of the model for this white paper and, more importantly, the model's propensity for unintended patterns.
Detect:
As we' ve discussed before, since the white paper does not belong to any class the model has learned, it should give an inference result that is almost as if it makes a random guess, to demon- strate it not biased towards any pattern. In the case of the multi-class classification with N classes, the ideal prediction probability distribution for the white paper would be q [ 1 N N ..... l- Hence measure the match of these two predictions p and q. we adopt the Kullback- Leibler Diver- gence:
Conquer:
Lup = * DE (pll4)
(1)
where À denotes the strength of the White Paper Assistance. Then we repeat this process for M iterations the hope of alleviating this unintended propensity.
Algorithm Pseudo-code of the White Paper Assistance
for each epoch do Real Images training using original loss function
2:
3: Update model parameters
4: initialize p (0.1((01) 1)
White Paper Assistance starts here.
5:
if p < P then
6: for each iteration E [1,M] do
7:
Generate a batch of white picture W
8: p Model(W))
White paper training.
9:
Update model parameters by Eq. end for
11: end if
White Paper Assistance ends here.
end for
There are two important questions for designing above Algorithm:
Q1.
Does the White Paper Assistance indeed alleviate the shortcut learning?
Q2. Why choose using the white paper?
To answer the first question, we evaluated our method in controlled experimental setup, by adding synthetic shortcuts to the data. Specifically, we added a 4x4 black square block on the top left corner of each training and testing sample of the first class (apple) of CIFAR100 (We refer this modified dataset as Shorteut-CIFARION allows network achieve negligible loss by only learning to discriminate this block the same position while ignoring other information. Therefore, after training on Shortcut-CIFAR 100,
When trained on Mitortut-CIFARR000
this small block
We explain the for repeating in Appendix.
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Original
wio
WP
Training
Testing
Testing
Shorteut-CIFARIE
CIEAR99
(a)
(b)
Figure Conceptual experiments: (a) Illustration of Mhortcut-CIFARIIe and CIFAR99. On Shorteut-CIFARIII all the samples of "apples" are modified, including training and testing sam- ples. On CIFAR99, all the testing samples except for samples of "apples" are modified. (b) Class activation mapping (CAM) visualizations of models trained on Shortcut-CIFAR 100 samples with synthetic shortcuts. While other models fixate on added blocks, WP(be short for White Paper As- sistance) successfully alleviates the excessive reliance of the model shortcuts and help it capture more informative features. We provide more visualizations in Appendix, A.
Table I: Proof of concept: Top-1 accuracy(%) on Shortcut-CIFAR 100 and CIFAR99. All the models(ResNet-56) were trained on Shorteu--CIFARI00.
Model
Shorteut-CIFAR100 CIFAR99
ResNer-56
62.46
39.95
ResNet-56 w/ WP
66.80
46.88
ResNet-56 w/ SD (Pezeshki et al. 2020
63.41
7.77
ResNet-56 w/ LIF (Nam et al. 2020
63.11
42.09
the network would exhibit a strong propensity to identify picture that has small black block on its top left corner "apple", if suffers from shortcut learning. We then designed new testing scenario where we extracted all the testing samples from the other 99 classes(except apple) and then added a small black block on the same position on each of them (We then term this as CIFAR99). Since on CIFAR99, only the remaining 99 classes were modified. Once the network excessively relies on the decision rule that connects "images with a black block" with the class "apple", it would demonstrate a strong propensity to identify the samples CIFAR99 with "apple", which would result in lower accuracy. Shortly speaking, the performance on CIFAR99 actually reveals how well the model could resist the propensity of shortcut learning.
Table I presents the performance of models which were trained on -IFoRR0 and tested on both Shorteut-CIFAR 100 and CIFAR99. As can WP improves the model's generalization ability on Shortcut-CIFARIIG as usual. We want to highlight the huge improvement WP achieves on CIFAR99, where models without WP demonstrate a strong propensity to misidentify the images when these images exhibit similar patterns as those in other classes. To verify that WP is indeed learning recognize more informative features, we visually plot the activation maps of all the models trained on
Figure [ (b) provably demonstrates the effectiveness of WP combating shortcut learning. After training on Shorteut-CIFAR 100, the model without WP would drawn in the small block in the upper left corner while applying WP helps the model focus on more discriminative features. We also include spectral decoupling regularization (Pezeshki al.) 2020) and LfF (Nam et al. (0200) comparisons. Both SDD and LfF could improve the model's generalization ability (higher accuracy on Shortcuf-CIFAR 100), but SD fails dramatically on getting over shortcut decision rule. In short, these results of this conceptual experiment answer the first question positively and manifest the ability of WP to restrain the excessive reliance on dominant patterns when classifying.
We hypothesis that such phenomenon would be relieved if we use the advanced variant of SD that im- poses penalty separately for each class. But in this case( 100 classes), it would entails massive increase of hyperparameters (at least 100).
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Regarding the second question, is tempting to expect that there would be or more ideal images that not only do not belong to the distribution of training data, but also are able to detect all the unintended dominant patters.. Alas, to precisely find such images require us knowing which patterss CNNs rely on, which is hard because patterns do not appear in a form amenable to discover so, not viable option. Intriguingly, over all the alternative option, the solution with the white paper works best. As in Figure 2 four candidates were evaluated, namely "Gaussian Noise", "Ice- cream", "CIFAR-IO", and "White Paper". We keep all the other implementation details unchanged and merely modified the images while training ResNet-56 on CIFAR-100 100. Specifically, "Gaussian Noise" experiments represent that we changed the white papers into images sampled from a standard normal distribution. "Ice-cream" denotes the whole ice-cream class of images from ImageNet while "CIFAR-10" 10" denotes that all the images from CIFAR- 10 were used for detection. Extensive details to facilitate replication are provided in the Appendix C
Even with noise-generated images, there is still a performance boost over the vanilla model. Then with the increasing num- ber of real-world images, the performances get higher. But white paper outperforms all the other solution. A possible ex- planation for this might be that the uninformative nature of
CIAARO
Ice-craa
the white paper seems make more suitable for detect- ing spurious dominant patterns, since the lack of semantics itself means no bias towards any pattern. Just like coloring on this white paper, the extent to which some pattern plays dominant role for a class will be shown on the output distri- bution of the white paper. We also want to note that all the alternatives outperform the vanilla setting, indicating that the effectiveness of the whole detect-and-conquee practice.
Neioe
Top-3 Accoracy19O
Figure 2: Effects of different types of images used in our scheme. The white paper outperforms the other solutions.
How DOES WHITE PAPER ASSISTANCE WORK?
After introducing our method, we then move on to a series of experiments used glean insights on the behavior of the White Paper Assistance.
What does the training with White Paper Assistance look like? Analysis on the trend of train- ing and testing accuracy of vital importance to understand the effect of a method. Figure 3 (a) and (b) characterize the change of training and testing accuracy across epochs during training with and without WP. Note that we set P 1, namely WP was steadily conducted after each epoch of real images training. Compared with its counterpart, training with WP exhibits slower increasing trend on training accuracy, demonstrating that our approach helps suppress model from overusing shortcuts that could rapidly improve the generalization training error can both reach zero regardless of the use our approach, training with WP achieves significant performance boost on testing data, demonstrating better generalization ability. Not only that, the use of WP on the later stage of training can still provide further improvement with the model, evident from the fact that training with WP achieves its best performance after epoch 225
training data otherwise. Even though the
It is still worth noting that after each time we conducted multiple iterations of white paper training, the testing accuracy would fall dramatically to around 1%. It is as if the model was guessing wildly all the testing data. But when moved accuracy would restore and continue to rise (as seen from the continuous curves of both training and testing accuracy Figure 3 (a) and (b)), as if the model was not affected by what just happened. Does the state of model performing random guess is a bad sign? Does this mean that White Paper Assistance is harmful? What happened with the model? Why would the accuracy could be restored? We will devote the next part to analyse the causes of it.
and fed real images, both the training and testing
Is white paper training harmful to the model? The ultimate goal of training is to find better parameters of the model. To delve deeper into WP, we turn our attention to parameter changes.
In this case, decay the learning rate by factor 0.1 at epochs 150, 225. The training after epoch 225 often suffers from severe overfitting that fails achieve further improvement.
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Whit Aash ESE
witte 2ASTS
1433N
LOG
L5G E20CW
206
Paanks
(a)
(b)
Talmn NoveAa Und Natie
120
28
106
(c)
(e)
Figure 3: Behavior White Paper Assistance: (a, b) The evolution of training/testing accuracy when training ResNet-110 100 with and without WP. (c) Changes in parameters of real images training and white paper training. We use LI distance to measure the changes of parameters on the final convolutional layer of ResNet-110 10 when training WP with p = 1. (d) Parameter distributions before and after White Paper Assistance was conducted. This change happened on the final convolutional layer of ResNet-110 at epoch 100. More results of changes distributions on other layers present in Appendix D
First, we need to figure out which part of the model is more affected. A trained ResNet-56 F(0) that has achieved 73.51 % accuracy on CIFAR- 100 was picked. We use C and to denote the parameters of the projection head (i.e. all the convolutional layers) and the classification head (i.e. the last fully- connected layer) at this moment, respectively. Then, WP was applied on F(0) and observed the 1%. Let F(Ø), č and the projection head and the classification head at this moment, respectively. To determine which part is more affected, we combined C with f and combined Č with f. As shown in Figure 4 for F(O). if we replaced its classification head, the accuracy changed little (73.51% 73.4%), whereas the accuracy would drop dramatically (73.51% % - 1%) when we replaced its projection head. These observations suggest that the modifications WP mainly happen on the projection head, rather than classification head. Similar conclusion could be drawn from F(0).
performance dropping
denote the parameters of the whole network,
Natwork [TO) 73.51%
Narwork 30 1.0%
regular training
Projection
white paper training
Projection head C
Projection
Projection
Figure 4: Illustration of how determine which part is affected by WP. We also extend this experiment to other models in Appendix
Then turn to quantitatively measure the changes of parameters due to method. Since white paper training and real images training alternately appear when 1, we plot the changes of parameters using (menn absolute error) distance with respect to the epoch. In Figure 3 (c),

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observe that changes brought by white paper training are smaller compared to real images training. Figure depicts the distributions of the last convolutional layer's 's parameter of ResNet- 110 before and after the white paper training certain epoch. It can be seen that our approach is actually disturbing rather than devastating the distributions of parameters. We believe the fact that the model has not been completely devastated proves that White Paper Assistance does not damage the model, and could explain why the accuracy could be rapidly restored. In addition, these results are strong proof that CNNs are vulnerable slight perturbations of parameters could bring down the whole generalization ability of the model, or at least it seems that way.
5 DOES WHITE PAPER ASSISTANCE PERFORM BETTER?
The section below shows the experimental results to prove that applying White Paper Assistance results in better performance. All experiments are implemented using Pytorch on 4xGTX not specified, all experimental results reported are averaged over 4 runs. Being limited to the space, we supplement the implementation details in Appendix
5.1 CLASSIFICATION
White Paper Assistance performs better across different model architectures. As diseussed before, White Paper Assistance enjoys a plug-and-play" property, namely it can be directly amend- able to almost any CNNs without any changes needed on the network architecture. To prove from an empirical standpoint, several different architectures were used to evaluate the effectiveness of our approach, namely, ResNet (He et al. (016), SeNet (Hu et a 2018), Wide ResNet (Zagoruyko Ko- modakis 2016). PyramidNet (Han et al.) 2017). DenseNet (Huang et al. 2017). and ResNext (Xie et al. 2017). All the experiments were performed on CIFAR- 100 (Kizhevsky 2012). one of the most extensively studied classification benchmark tasks. In Table 2 we observe that White Pa- per Assistance consistently outperforms the bascline scheme. For example, our approach achieves 76.35% accuracy when applying Resnet-110 110 model, which is 2% improvement over vanilla train- ing. This wide applicability significantly promotes its value in practice and implies, once again, that learning shorteut cues is common problem that CNNs cannot easily get rid of without special treatment like White Paper Assistance.
Table 2: Top-1 error rates(%) over different architectures.
Model
w/o WP
w/ WP
Resnet- 10
25.65 0.07
23.65
+ 0.10
Resnet-164
24.26 0.06
22.89
1 0.08
SeResNet- 110
23.36 0.09
22.37
+ 0.04
WRN-28-10
21.18 0.01
19.80
0.04
DenseNet-100-12
22.78 + 0.07
22.02 + 0.02
ResNext-29, 8x64
20.68 + 0.16
19.41 + 0.12
PyramidNet- 1102770
18.62
0.03
17.96 + 0.08
2000midNet-200-240
16.77 1 0.07
16.13
1012
White Paper Assistance gives better performance on various benchmarks. We next performed experiments with different benchmark datasets. The following benchmark datasets were used: SVHN (Netzer et al., 201T), CIFAR-10 (Krizhevsky] 2012), tiny-ImageNet, CUB-200-2011 (Wah) al. 2011), 1). StandfordDogs Khosla al. [2011), StandfordCars (Krause et al. 2013). As shown in Table 3 White Paper Assistance leads to better error rates in all the cases. Such wide applicability to datasets reveals that it is nearly impossible to create shortcut-fres free dataset, which further proves the importance of avoiding reliance on unintended shortcuts.
White Paper Assistance leads to further improvements over other techniques. In practical use, it is common to use multiple techniques simultancously in the hope of a higher boost on perfor- mance. Thus it imposes very high demands in term of compatibility for technique. With this in

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Table 3: Top-1 enror rates(%) on different benchmark datasets.
Dataset
Model
w/o WP
w/ WP
SVHN
ResNet-110
3.57 #002
3.28 0.01
CIFAR-10
ResNet-110
5.61
1.007
4.94 0.04
Liny-ImaeNNa
ResNet-
37.66 1 0.188
35.58 1 0.05
CUB
ResNet-50
32.36 + 0.18
28.65 + 0.05
Standford Cars
ResNet-50
12.12 + 0.02
9.87 + 0.12
Standford Dogs
ResNet-50
38.79 0.74
34.55 + 0.82
mind, then applied White Paper Assistance with several widely used techniques, Mixup (hang et al. 2017), AutoAugment (Cubuk et al. 2018). FastAutoAAugment(L... et al.) 2019), and Label Smoothing (Szegedy al., 2016) into ResNet-1TO. The experiments presented here are designed to pinpoint the compatibility of WP. Consequently, we highlight that achieving state-of-the-ar results the objective. As reported in Table our approach consistently makes further improvements, which is reasonable as the training of white paper is independent of the training process of real images.
Table 4: Top-1 error rates(%) over different techniques.
Method
vanilla
w/ WP
White Paper Assistance
23.65 0.09
Mixup
22.13 0010
21.80
0.23
AutoAugment
21.70 0.03
21.12
0.29
FastAutoAugment
22.46 0.05
21.73
0.15
Label Smoothing
24.68 0.07
23.41 0.07
5.2 IMBALANCED CLASSIFICATION
The benchmark datasets we use above all exhibit roughly uniform distributions of class labels. But is always prohibitively expensive to construct a real-world dataset with proper balance among classes, which explains the long-tailed label distributions that most real-world large-scale datasets (Cui et al. 2018 Gupta et al. 2019) have, On these datasets, a few dominant classes hold large number of samples while a few other classes only possess relatively few samples. Conceivably, with the severe imbalance among the number of classes comes a severer imbalance among patterns, help. To verify our conjecture,
where White Paper Assistance sion of CIFAR-10 and CIFAR- as (Cao et al., 2019), then validated the performance with and without approach. We also include combination of our approach with two other specialized approaches, CB-Focal (Cui et al. (2019) and LDAM (Cao et al. 2019) We strictly use the same parameter settings of (Cao al. 2019).
created the long-tailed ver-
We report the top-1 validation errors of various methods and combinations for long-tailed CIFAR- 100 and CIFAR-10 in Table We observe that White Paper Assistance alone can already improve over the vanilla setting, and the combinations of our approach with CB-Focal and LDAM achieve better performance gains, demonstrating both the versatility on imbalanced classification and the compatibility with other techniques. Overall, these observations strengthen the idea that White Paper Assistance is great use to solve or alleviate this kind of pattern imbalance problems.
5.3 ROBUSTNESS
It is well known that the human vision system is not easily fooled by small changes in query images, whereas existing deep learning models may exhibit dramatic performance decline (Hendrycks et al.] 2021). This proves again that the deep learning vision systems do not actually solve task in the
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Table 5: Top-1 error rates(%) of ResNet-32 on long-tailed CIFAR-10 and CIFAR-100 The imbal- ance ratio denotes the ratio between the numbers of samples of the most and least frequent classes.
Long-tailed CIFARIO
Long-tailed CIFAR100
Imbalance Ratio(N /aae/aaNNaN min)
100
50
100
50
CE(Baseline)
28.71
1.71
22.74
1.005
61.24
1.05
56.58
1.09
CE(Baseline) + WP
27.68 0.40
22.04
0018
60.15 + 0.07
54.97
0.11
CB-Focal CB-Focal + WP
27.86 0016
23.34
0.21
61.51
+ 0.20
56.28
0.19
26.79 0.07
22.26
0.15
60.04
0.04
55.11
0.31
LDAM-DRW
22.59 = 0.08
18.40 0.15
57.43 #
52.89 #051
LDAM-DRW++ + WP
21.15 = 0.21
18.00
0.23
55.13 0.24
51.79 0.09
way we intend them to, hence should be viewed as a symptom of models learning shortcuts. In or- der check whether White Paper Assistance alleviates the shortcut learning indeed, we evaluate the robustness to common corruptions of our approach on tiny-ImagNNet- 2019). Specifically, we compare the classifiers' performance with and without WP across five cor- ruption severity levels on each type of given corruption. Since each model performs differently on its own, each result was averaged by four runs. We have written the detailed calculations in the Appendix
(Hendrycks & Dietterich,
The results are shown in Table As expected, White Paper Assistance clearly improves baseline's robustness against all the corruptions universally. These consistent improvements imply the poten- tial of our approach to generalize to other untested types of corruptions. It's worth noting again that these improvements are achieved solely through the help of the white paper, which is uninformative and has no relationship with these noisy data. This suggests that our approach is indeed able to improve the robustness, and therefore implies that White Paper Assistance have effect avoiding the excessive reliance of shortcuts.
Table 6: Corruption errors of tiny- -iagee-eaeNet-CCC on different corruptions across five corruption sever- ity levels. All metrics are top-1 eтror rates (for corrupted test sets, we average for 5-severity levels in four runs ResNet-110. Separate results in each run provided in Appendix
NOISE
BLUR
WEATHER
DIGITAL
Method
impulse
clustic
piselate
jpes
WP WP
79.62
74.22 74.75
75.81
64.44
67.54
75.31
72.23
72.82
63,80
CONCLUSION
In this paper, we propose an interesting method, called White Paper Assistance, used to alleviate the excessive reliance of models on shortcut learning. Inspired by the common sense of the printer test page, our framework utilizes the white paper to detect the unintended propensity for dominant features that lack of generalization utility and then "debiases" the model by enforeing the model to produce uniform output distribution on the white paper. By adding synthetic shortcuts to the train- ing data, we ensure that our approach can help remove shortcut features from the data and improve the generalization ability. Besides, through extensive experiments, our method shows successful results along three axes: classification, imbalanced classification and robustness. The wide appli- cability, compatibility and versatility highlight the progress of method in overcoming shortcut learning, which thus should be viewed as a step forward towards a shortcut free training, as we claimed in the title.
We hope this study could facilitate the awareness for shortcut learning, and more importantly, open up promising avenues towards fair, robust, trustworthy deep learning.

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7 REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we provide a pytorch working example source code of our method in supplementary materials. We will make this project open-source after the whole review process. We also provide implementation code of Shorteut-CIFARI000 and CIFAR99 if you are interested.
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A
MORE RESULTS ON CONCEPTUAL EXPERIMENTS
A.1 MORE CAM VISUALIZATIONS
For better understanding, we plot more examples on cam visualization here.
Original
base
WP
w/ SD
w/ LFF
Figure 5: Supplementary cam visualizations on models trained on Chorteut-CIFAR1O0
A.2 MORE EXPERIMENTS RESULTS
In the original experiments, we modified the first class in Shorteut-TIFAR100 and modified the other 99 class testing samples in CIFAR99. We extend this experiments by modifying the third class(fish). Specifically, in the revised Shortcut-CIFAR 100, only samples of the third class are modified. In the revised CIFAR99, we held out the testing sample of the third class and revise all the testing samples of the remaining 99 classes. The results are presented in the following table.
Table 7: Top-1 accuracy(%) on revised Shorteut-IFFAROO and revised CIFAR99.
Model
revised Shorteut-CIFARION revised CIFAR99
ResNet-56
63.57
25.55
ResNet-56 w/ WP
66.75
29.77
ResNet-56 w/ SD Pezeshki et al. 2020
62.76
6.69
ResNet-56 w/ LfF
NNa et al. 2020
63.29
27.57
B MORE QUESTIONS ON DESIGNING WHITE PAPER ASSISTANCE
Q3.
Why repeat the process for multiple iterations?
Regarding this question, we answer it with intuitive reasoning and empirical evidence. Intuitively, biased model cannot be perfectly amended with ease. Take "polishing" supportive example. The removal of unintended oxidization requires polishing on the appearance of an item back and forth, until a smoothing finish is accomplished. To verify the intuition, we conduct a controlled trial that we kept P unchanged while modified M from 50 to 500. Aligned with our expectation, more rounds of schemes lead to better refinement in learning, which results in a better generalization performance. Usually, find that the performances often get the highest when M is approximately equal to the times real training images are trained per epoch, namely when the amount of training on the white paper is compatible with the amount of training the real images.
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75.25
Top 1 Accuracy
74.75
14.25
100
1.5
350 450
500
Times
Figure 6: Final test accuracy as a function of iterations that we repeat the process for. The shaded areas represent the minimum and maximum results from 4 runs. Dashed dotted line denotes the baseline accuracy. Our approach receives near optimal performances when the amounts of training on white paper and real images are close to each other(In this case, the times of iteration of real image training for one epoch are 391).
C EXPLANATIONS ON DIFFERENT TYPES OF IMAGES
Here we provide some further explanations on experiments regarding why choose using the white paper. In these experiments, we kept all the scheme details unchanged but only modified the images fed into the model(as in Algorithm 1, line 7).
Vanilla The baseline settings of training ResNet-56 CIFAR-100.
Gaussian Noise Images randomly sampled from standard normal distribution. Ice-cream Ice-cream images randomly sampled from ImageNe((Ice-crem class, a07614500). CIFAR-10 Images sampled from CIFAR-10.
D MORE EXPERIMENTS EXPLORING BEHAVIOR OF WHITE PAPER ASSISTANCE
To begin with, we extended the experiments Figure 4 (that determine the part that White Paper Assistance has more effect on) to other models. Two models used to further demonstrate that White Paper Assistance mainly modifies the projection head and has little effect on the classification head of models(Table
Table 8: Results on other architectures to suggest that the modifications White Paper Assistance mainly happens on the convolutional layers.
Original C f
After White Paper Training
Combination #1
Combination #2
Model
Č+ ++f f
C + f
Č+ f
ResNet- 110
74.18%
1.13%
74.26%
1.10% 1.00%
Brramid-110-770
81.31%
1.00%
81.30%
In Section 5, we still report that White Paper Assistance would incur smaller changes in parameters and such changes would not devastate the inhere parameter distributions. The observations happen on the last convolutional layer of ResNet-110, Here we present more results about the changes and parameter distributions on other layers in Figure
E IMPLEMENTATION DETAILS
We describe the training implementation settings in detail.
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Layer1.5.conv3
Layer2.5.conv3
Images Traning White Paper Traning
Wmager Traniing
Whiee Paper Trainig
Changes in Parameters
Parameters
125
125
100
100
75
75
25
25
0-
50
100
150 Epochs
200
250
300
50
100
150 Epochs
200
250
300
(a)
(b)
Layer1.5.comv
Layer2.5.com 5.con33
Wilt Paper Tran rg
Bedor Whtr Paper Traniee
Altx Whste Plp Trunrs
Alta Whle Papt Thnnne
Num bers parammttes
paramettarss
parameters
Values parameters
Figure 7: Behavior of White Paper Assistance other convolutional layer (a,b) Changes in pa- rameters of real images training and white paper training on other convolutional layers. The shaded areas indicate the standard deviations across independent trails. (c,d) Parameter distributions be- fore and after White Paper Assistance conducted on other convolutional layers of ResNet-110 at epoch 100.
E.1 CLASSIFICATION
CIFAR-100 For ResNet, SeResNet and ResNext, we set the number of training epochs to 300. The learning rate were set and was decaying by the factor of 0.1 epoch 150 and 225. We used SGD optimizer, and the minibatch size. momentum, weight decay were set to 128, 0.9, and 0.0001, respectively. When training, we set P = =
For Wide ResNet, changed the training epochs to 200. Then the learning rate was decaying by the factor of 0.2 at epoch 60, 120, 160, respectively. When training, we set = 1, À = 0.5.
For DenseNet, changed the matc- size to 64 following the common practice. When training, we set P = 1, X
For NytamidNet, the learning rate rose to 0.25 and we set P 1, 0.5 when training.
For all the experiments on CIFAR-100, we adopted the standard data augmentation techniques in- cluding Horizontal Flipping and Random Cropping.
CIFAR-10 We adopted the same training strategy as on CIFAR100.
SVHN We held all the hyper-parameters unchanged but did not adopt any data augmentation techniques. When training, we P 1, 0.1.
tiny-ImageNe We adopted the same training strategy CIFAR100 except that changed the mini-batee batihbatc size to 256.
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On fine fine-grained benchmark datasets, trained ResNet-50 from scratch and set the training epochs to 300 to ensure convergence. The learning rate was set to 0.1 and decayed by 0.1 at epoch 150, 225. The mini-batch size, momentum, and weight decay following contents move on to discuss the data augmentation techniques we used.
set to 16, 0.9, and 0.0001, respectively. The
CUB-200-2011 and Standford Cars For these two datasets, first resized images 600 600 and cropped them to 448 x 448. Then we adopted the Random Horizontal Flipping.
Standford Dogs For the Standford Dogs, resized images to 256 256 and cropped them to 224 x 224. Then we also adopted the Random Horizontal Flipping.
1, = 1. For Au- When training, we set P A 1.5. For FastAutoaugment, we adopted the implementation of publicly available code and set P = 1, 1. For label smooth, we set the smoothing parameter to 0.1 and set P
Combinations CIFAR-100 When combined with Mixup, set P toaugment, we adopted the implementation of publicly available code
4
E.2 IMBALANCED CLASSIFICATION
Both Long-tailed CIFAR-100 and CIFAR-10 10 follow an exponential decay in sample size across different clasess. The ratio p is used to denote the ratio between sample sizes the most frequent and least frequent class. Figure g depicts the sample distributions of Long-tailed CIFAR-100.
Long-talled CIFAR-100 Ratio 1=100
Long-talled CIFAR-100 Ratio
Numbers
Numbars
(a)
(b)
Figure 8: Number of training examples per class in tailed CIFAR-100.
For implementation details, we adopted the same setting as those suggested in (Cao et al. 2019
F METRICS AND COMPREHENSIVE RESULTS ON ROBUSTNESS
To evaluate the performance of White Paper Assistance on robustness to common corruptions, we refer to the metrics used in Imageeee Specifically, when evaluating performances of base- lines, we took four trained classifiers f trained with vanilla settings. Then we tested the classifier on each corruption type each level of severity s E 2,, 4, 5]. We used E!_ denote the top-1 стror rates. Then the average corruption eтror of all baseline models in corruption e should be:
CF.
4
f=1
Then we aggregated all the CE. to compute the mean corruption error values all the corruption:
15
15
1
mCF
CF. = 4x5x
Ef
15
8,C
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Under review as a conference paper at ICLR 2022
Here, report the results of all the models tested in Table
Table 9: Comprehensive corruption error results of baseline and White Paper Assistance on single
model. "Clean" here denote the top-1 error rate of the model on the clean version of Ina-lmm
Model
Rm
17.41
77.70
$7.05
77:89
$1..07
56.
151.1
79.93
81.81
37.05
72.85
73.20
66-35
353-71
35.05
67.11
66.00
ResNer 110
37.21
73.41
84.16
$0.99
81.85
55.94
53:97
80.27
83.56
78.73
75.51
75.16
05.13
59.97
75.35
64.56
67.75
RoNa-1110
68
75.51
$3.50
79.95
$7.67
78.18
75.25
76,38
04.11
07.05
12.31
73.09
ResNet 110-WP
75:06
82.82
78.55
76.86
71.45
63.70
RosNa-110-WP
35.06
76.28
83.77
76.05
81.56
$4.23
83.34
76.
82.06
76.17
12.75
72.55
63.57
87.02
72.52
61.37
64.64
ReiNer 110-WP
25.50
75.55
81.30
77.75
79.81
83.91
X2.50
37.07
61.84
74.84
71.54
71.34
65.54
36,88
72.38
02.03
63.41
G EFFECTS OF PARAMETERS
We evaluated the effect of probability P and strength when training ResNet-56 in CIFAR-100 using the aforementioned settings. We first inspected the performances for different choices of P {0. 0.2, 0.4, 0.6, 0.8, }} when À = 1. Specifically, P 0 denotes the baseline without applying White Paper Assistance. In Figure 9 (a), we observe that White Paper Assistance consistently achieves performance boost even in small participation rate. Then we turned our attention to another hyper-parameter À that also plays an important role during training. Here we tried different choices with À {0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8) while keeping P 1. Figure 9 (b) characterizes the evolution of performances on varying A. The best performance can achieved when À is set 1.
15.00
74.78
1435
(ccuracy3%)
(%)
55
74.58
74 40
24:90
28.00
74.22
74.25
20.25
74.05
73.50
73.50
0.0
0.4
3.6
0.0
14
66
0.8
1.0
1.2
Probability
Strength A
(8)
(b)
Figure 9: Effects of Probability P and Strength X.
18


Reviews:
[{"review_id": "hojS7FzpmZ", "reviewer": "Reviewer_w6iS", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "main_review": "Strengths:\n1. The paper is very well-written, especially the first three sections (introduction, motivation, etc.).\n2. Authors conducted a very comprehensive set of empirical experiments and ablation studies to show the usefulness of WPA.\n3. The idea is novel.\n\nWeakness:\n1. The idea is a bit simple -- which in of itself is not a true weakness. ResNet as an idea is not complicated at all. I find it disheartening that the paper did not really tell readers how to construct a white paper in section 3 (if I simply missed it, please let me know). However, the code in the supplementary materials helped. White paper is constructed as follow:\n```python\nwhite_paper_gen = torch.ones(args.train_batch, 3, 32, 32)\n```\nIt offers another way of constructing white paper, which is \n```python\nwhite_paper_gen = 255 * np.ones((32, 32, 3), dtype=np.uint8)\nwhite_paper_gen = Image.fromarray(white_paper_gen)\nwhite_paper_gen = transforms.ToTensor()(white_paper_gen)\nwhite_paper_gen = transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))(white_paper_gen)\n```\nThe code states that either version works similarly and does not affect the performance.\nI wonder if there are other white papers as well, for example `np.zeros((32, 32, 3))` -- most CNN models add explicit bias terms in their CNN kernel. Would a different white paper reveal different bias in the model? I don't think the paper answers this question or discusses it. \n2. Section 4 \"Is white paper training harmful to the model?\" -- the evidences do not seem to support the claim. The evidences are 1). Only projection head (CNN layers) are affected but not classification head (FCN layer); 2). Parameter changes are small. None of these constitute as a direct support that the training is not \"harmful\" to the model. This point can simply be illustrated by the experimental results\n3. Section 5.1 and 5.2 mainly build the narrative that WPA improves the test performance (generalization performance), but they are indirect evidence to support that WPA does in fact alleviate shortcut learning. Only Section 5.3 and Table 6 directly show whether WPA does what it's designed to do. A suggestion is to discuss the result of Section 5.3 more.\n4. It would be interesting to try to explain why WPA works -- with `np.ones` input, what is the model predicting? Would any input serve as white paper? Figure 2 seems to suggest that Gaussian noise input does not work as well as WPA. Why? Authors spend lot of time showing WPA improves the test performance of the original model, but fails to provide useful insights on how WPA works -- this is particularly important because it can spark future research directions.", "summary_of_the_paper": "The paper proposes a method called White Paper Assistance that serves as a data/task agnostic regularization to prevent shortcut learning. It conducts a set of empirical experiments to show the usefulness of this technique.", "summary_of_the_review": "The paper proposes a simple idea and conduct a wide range of experiments that seem to focus on producing SOTA results instead of illustrating how and why WPA works. However, given the amount of effort that goes into this paper, I'm on the fence of whether to recommend accept or reject.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "KQU0uyDG8WA", "reviewer": "Reviewer_y5Su", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "main_review": "The paper tackles and illustrates an important issue of not only CNN's but general deep learning models. As the authors describe this is not a novel insight and several approaches were introduced to reduce and even prevent shortcut learning by deep neural networks. However, I major line of related work seems to be missing: Teso et al. Explanatory Interactive Machine Learning (AIES 2019), Ross et al. Right for the Right Reasons (IJCAI 2017), Schramowski et al. Making deep neural networks right for the right scientific reasons by interacting with their explanations (Nat Mach Intell 2020), to name a few.\n\nMore importantly, the paper is not well written and even looks unfinished in parts (e.g first paragraph on page 5). This makes it very hard to read. \n\nSince, in the vision domain, shortcut learning is already a well-explored field (see above) benchmark datasets were already introduced (Decoy MNIST, ColorMNIST, CLEVR-Hans) cf e.g. https://arxiv.org/abs/1703.03717, https://arxiv.org/abs/2011.12854 and https://www.nature.com/articles/s41467-019-08987-4. \n\nInstead of designing or synthetic adapting datasets, I recommend an evaluation of already introduced and well-known datasets. Further, a comparison to previous approaches is missing in the present work.", "summary_of_the_paper": "The present work introduces an approach to tackle Shortcut Learning by CNNs, called White Paper Assistance (WP). After motivating and introducing the method, the authors evaluate it on computer vision datasets with synthetic inserted shortcuts, ie. black pixels in the corners of the images. The authors show that the WP approach reduces the learning of so-called shortcuts.  ", "summary_of_the_review": "Unfortunately, I do not believe that this paper meets the standard for publication. Besides that, the paper is not well written and therefore hard to understand its experimental evaluation is missing a comparison to previous approaches.\n", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "1: The main claims of the paper are incorrect or not at all supported by theory or empirical results.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "J8eQiDdph6", "reviewer": "Reviewer_u8Nt", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "main_review": "**Strength:**\n\nThe paper is easy to read and understand, and the arguments are mostly clear and concise. The prior works are explained well and the current work is well motivated and very relevant. The proposed method is simple and effective, the right combination for wide adoption, and the insights from Section 4 on how WP affects different parts of the CNN classifier can help improve the overall understanding of these models in the community. The experiments are well-designed and clear in what they try to illustrate, and sufficiently analyze the performance of the method, on its own and in conjunction with other related regularizations, as well as the contribution of different hyperparameters.\n\n**Concerns and Questions:**\n\n1- In Section 2, the paper argues that “instead of leveraging laborious and expensive supervision, our method utilizes the common sense by leveraging the white paper to detect the dominant patterns”, however, I cannot find any experiment/figure backing this up. Reporting some cost comparison (training time, computation, etc.) could help justify this claim.\n\n2- Table 1 is very helpful in illustrating the effectiveness of WP, however when it comes to testing the method using other input types (e.g. noise or ice-cream), the paper only provides the overall performance on CIFAR100. A more clear and helpful comparison would be to also include these different input choices in Table 1.\n\n3- Table 4 is confusing to me: why does the first row read “+White Paper Assistant” and then has “-” under “w/ WP”? Are these methods being stacked as we go down the rows of the table (for example is the last row using all the above methods together, or just Label smoothing w/ and w/o WP? I suspect it is the latter case, in which case I think “+” should be dropped from the rows to avoid confusion). \n\n4- In a closely related line of work, adversarially-trained CNNs have been shown to be less reliant on shortcut features and focus on more “human-expected” features (see for example Figure 2 and 3 in [1]). In particular, these models can reduce error of classification on data limited regimes (such as the small datasets used in this paper). Including a study of whether WP achieves the same performance boost on adversarially-trained CNNs, and moreover, whether WP can be used as an alternative to adversarial training for robustness, would be very helpful for the community. But at the very least, I recommend referencing such works and commenting on potential effects.\n\n5- An important consideration when deciding to use WP is the time/accuracy trade-off, that is, how will increasing the number of WP iterations (M) from zero to 50 or 500 affect training time, and in turn accuracy (this can be achieve by adding a training time plot to Figure 6 of Appendix B).\n\n6- How will the size of the dataset affect the helpfulness of WP (as most of the datasets used in the paper seem to be on the smaller size)? This is an important point to consider since methods that try to avoid shortcut connections tend to degrade performance on larger datasets (see [1]).\n\nSide note: Figure 3e should be 3d.\n\n[1] Tsipras et al. Robustness may be at odds with accuracy. ICLR (2019).\n", "summary_of_the_paper": "The paper proposes a novel method, White Paper Assistant (WP), to prevent CNNs from utilizing spurious input-out correlations, the so-called shortcuts, in classification. The main idea is to intermittently update the CNN to predict uniform distribution over classes for white image inputs. Through careful and extensive empirical studies on various datasets, the paper shows that this simple regularization can prevent the CNN from excessively focusing on shortcuts, thus learning more generalizable features and improving the overall accuracy on the test set.", "summary_of_the_review": "I enjoyed reading this paper, and I think its proposed method is mostly effective and can be valuable as a new regularizer for CNNs. Moreover, the insights into how different parts of a CNN are affected by such a regularizer strikes me as a very helpful piece of knowledge for the wider research on neural net architectures. As such, despite having some concerns listed previously, I think the paper merits acceptance.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "H6F0Sv9EMMe", "reviewer": "Reviewer_C42n", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "main_review": "Strengths:\n-  The proposed method is intuive, simple to implement, and very general.  \n-  The problem of preventing \"shortcuts\" is an important one.\n\nWeaknesses:\n-  The experiments have a fundamental flaw that prevents the results from being meaningful:  the results reported are the \"best test accuracy achieved across the entire training process\" instead of the standard \"test accuracy of the model with the best validation performance across the training process.\"  This conclusion is based on Figure 3 and an inspection of the code.  \n\t-  This might be related to the conflict between the stated intuition that \"predicting uniform probability for a white image should help the model\" and the empirical results that \"predicting uniform probability for real images (eg, CIFAR images) also helps the model.\"  \n-  The connection to the \"shortcut\" literature should be expanded upon. \n\t-  The related work is missing many references which include, but are not limited to: Shetty et al \"Not Using the Car to See the Sidewalk--Quantifying and Controlling the Effects of Context in Classification and Segmentation\";  Rieger et al \"Interpretations are useful: penalizing explanations to align neural networks with prior knowledge\";  Teney et al \"Learning what makes a difference from counterfactual examples and gradient supervision\"; Singh et al \"Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias\"\n\t-  It is unclear what shortcuts this work detects, which is something that many methods in the \"shortcut\" literature specify. \n\t-  The evaluation is run on a testing distribution which matches the training distribution, which is unsusual for methods in the \"shortcut\" literature (as using the \"shortcut\" usually makes it easy to acheieve high accuracy on the training distribution, so showing improvements in this way by preventing \"shortcuts\" is very challenging).\n\t-  While setup correctly, the example using \"black squares\" and the CIFAR dataset is a very simplistic (and artificial) example of a \"shortcut\".  See the aforementioned references for some other examples.  \n\t-  Generally, it seems that the proposed method is closer to a standard regularization technique than a method for preventing \"shortcuts\".\n\n", "summary_of_the_paper": "The work proposes a method for reducing the extent to which a model learns to rely on \"shortcuts\" by introducing a new form of regularization encourages the model to make uniform predictions for white images. The proposed method is evaluated across a range of model architectures and datasets and evaluated in combination with a variety of existing techniques.  \n", "summary_of_the_review": "On its own, the methodology used to gather the results disqualifies the paper for publication at this point.  If the reviewer is mistaken about this, then all of the sub-scores would increase.  \n\nHowever, even if the reviewer is mistaken about this methodological error and the results are valid, it does not seem that the paper is ready for publication because the connection to the \"shortcut\" literature needs clarification (which impacts how almost the entire paper is written).   \n\n# Update based on author responses\n\nThe author's responses have addressed some of my concerns (eg, using testing data as a validation set, the lack of overlap between CIFAR10/100).  However, the paper still needs to be revised to emphasize that it is not focused on \"perceptible patterns\" as shortcuts (eg, cows are usually in pastures) but rather on \"imperceptible patterns\" (eg, high frequency signals in an image).  So I've changed my score from a 1 to a 3.  \n\nI'm very familiar with the work on \"perceptible patterns\" and this paper is not up to par for that area because  it does not identify specific shortcuts and then demonstrate that it has reduced them.  I'm much less familiar with the work on \"imperceptible patterns\".  So I've changed my confidence from a 5 to a 3.  \n\n", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper studies the "shortcut" learning phenomenon in CNNs and proposes a simple and effective strategy (white paper) to alleviate specific shortcut patterns (e.g. "black squares" in the image). The proposed scheme is verified empirically and shown to improve over some existing solutions. All reviewers appreciate the simplicity of the idea, which allows its quick implementation and reproduciblity. However, reviewers y5Su and C42n believe the notion of shortcuts as studied in this paper are not only very limited, but also artificial. Consequently, they raise doubts about practical relevance/significance of the method for real world datasets with natural shortcuts. Based on these concerns, I suggest authors to identify a real setting (non-artificial data) where, alongside their synthetic shortcuts, can show the practical effectiveness of the proposed can.
Decision: Reject