White Paper Assistance: A Step Forward Beyond the Shortcut Learning

1 INTRODUCTION . We don ’ 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 et al. , 2015 ; Zhang et al. , 2007 ; Shafer et al. , 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. , 2020 ) ( also known as “ spurious cues ” ( Hendrycks et al. , 2021 ) or “ superficial correlations “ ( Jo & Bengio , 2017 ; Pezeshki et al. , 2020 ) ) 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 . In this work , we adopt the gradient starvation hypothesis , proposed in ( des Combes et al. , 2018 ; Pezeshki et al. , 2020 ) , that the leading cause for this feature imbalance is 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 a 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 a printer , the printer has to perceive it and then duplicate it using the right color . The color cast problem thereby indicates the wrong propensity of color using . In practical use , when we suspect that our printers are having 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 to seek corresponding solutions . 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 it makes a random guess , to demonstrate it does not mistake this sample as 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 on the bias , such as explicit labels of misleadingly correlated attributes ( Kim et al. , 2019 ; Li & Vasconcelos , 2019 ; Sagawa et al. , 2019 ) , or domain-specific-bias-tailored training technique ( Wang et al. , 2019 ; Geirhos et al. , 2019 ; 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 we summarize our contributions : • We propose a 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 combinations of techniques to show the wide applicability and compatibility of our method . • We test our method in imbalanced classifications and robustness against corruptions to demonstrate the versatility . 2 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 . It has been demonstrated that models may learn spurious shortcut correlations , which may be sufficient to solve a training task but are 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 a 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 human-intended generalization . As an active line of research , numerous studies have provided different explanations for this phenomenon ( Nasim et al. , 2019 ; Xu et al. , 2019 ; Parascandolo et al. , 2020 ) . For example , Valle-Perez et al . ( 2019 ) suggests that the parameter-function map of networks would bias towards simple functions . Kalimeris et al . justifies the simplicity bias further by showing that SGD learns functions of increasing complexity . Hermann & Lampinen ( 2020 ) demonstrates the model being “ lazy ” that it would favor the easier-to-extract feature over a more predictive feature . In 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 on discovering other predictive features . Recently studies that relates to shortcut removal usually requires extra supervision ( Kim et al. , 2019 ; Sagawa et al. , 2019 ) . 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 a more preferable shape-based representation . Li et al . ( 2020 ) further provide supervisions 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 to detect the dominant patterns . 3 OUR METHOD . The algorithm we propose is a conceptually simple and plug-and-play 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 1 . Generally speaking , the aim of this algorithm is to detect and conquer . Detect : For certain epoch from training iteration , the probability to conduct White Paper Assistance is P , and 1 − P if otherwise . Once applying it , a batch of white paper will be fed into the model and we can obtain the normalized output distribution ( using “ softmax ” ) p. The distribution here represents the perception of the model for this white paper and , more importantly , the model ’ s propensity for unintended patterns . Conquer : 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 demonstrate it is not biased towards any pattern . In the case of the multi-class classification withN classes , the ideal prediction probability distribution for the white paper would be q = [ 1N , 1 N , 1 N , ... , 1 N ] . Hence to measure the match of these two predictions p and q , we adopt the Kullback-Leibler Divergence : Lwp = λ ∗DKL ( p‖q ) ( 1 ) where λ denotes the strength of the White Paper Assistance . Then we repeat this process for M iterations in the hope of alleviating this unintended propensity . 1 Algorithm 1 Pseudo-code of the White Paper Assistance 1 : for each epoch do 2 : Real Images training using original loss function 3 : Update model parameters 4 : initialize p← Rand ( 0 , 1 ) . White Paper Assistance starts here . 5 : if p < P then 6 : for each iteration ∈ [ 1 , M ] do 7 : Generate a batch of white picture W 8 : p←Model ( W ) . White paper training . 9 : Update model parameters by Eq . ( 1 ) 10 : end for 11 : end if . White Paper Assistance ends here . 12 : 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 a controlled experimental setup , by adding synthetic shortcuts to the data . Specifically , we added a 4×4 black square block on the top left corner of each training and testing sample of the first class ( apple ) of CIFAR100 ( We refer to this modified dataset as Shortcut-CIFAR100 ) . When trained on Shortcut-CIFAR100 , this small block allows a network to achieve a negligible loss by only learning to discriminate this block on the same position while ignoring other information . Therefore , after training on Shortcut-CIFAR100 , 1We explain the reason for repeating in Appendix . B. the network would exhibit a strong propensity to identify a picture that has a small black block on its top left corner as “ apple ” , if it suffers from shortcut learning . We then designed a 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 on 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 1 presents the performance of models which were trained on Shortcut-CIFAR100 and tested on both Shortcut-CIFAR100 and CIFAR99 . As we can see , WP improves the model ’ s generalization ability on Shortcut-CIFAR100 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 to recognize more informative features , we visually plot the activation maps of all the models trained on Shortcut-CIFAR100 . Figure 1 ( b ) provably demonstrates the effectiveness of WP in combating shortcut learning . After training on Shortcut-CIFAR100 , 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 et al. , 2020 ) and LfF ( Nam et al. , 2020 ) as comparisons . Both SD and LfF could improve the model ’ s generalization ability ( higher accuracy on Shortcuf-CIFAR100 ) , but SD fails dramatically on getting over shortcut decision rule . 2 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 . 2We hypothesis that such phenomenon would be relieved if we use the advanced variant of SD that imposes penalty separately for each class . But in this case ( 100 classes ) , it would entails a massive increase of hyperparameters ( at least 100 ) . Regarding the second question , it is tempting to expect that there would be one 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 patterns . Alas , to precisely find such images require us knowing which patterns CNNs rely on , which is hard because patterns do not appear in a form amenable to discover . . . so , not a 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 ” , “ Icecream ” , “ CIFAR-10 ” , and “ White Paper ” . We keep all the other implementation details unchanged and merely modified the images while training ResNet-56 on CIFAR-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 ” 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 number of real-world images , the performances get higher . But white paper outperforms all the other solution . A possible explanation for this might be that the uninformative nature of the white paper seems to make it more suitable for detecting 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 a dominant role for a class will be shown on the output distribution 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-conquer practice .

Summary: 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.