Learning Debiased Representation via Disentangled Feature Augmentation

1 Introduction . Despite the recent advancement of deep neural networks , they often rely overly on the correlation between peripheral attributes and labels , referred to as dataset bias [ 1 ] , especially when such strong bias is found in a given dataset . A majority of samples in the biased dataset exhibit visual attributes that are not innate but frequently co-occur with target labels ( i.e. , bias attributes ) . For example , most of the bird images in the training dataset may contain the background as the blue sky , while the birds may still be found in different places . Thus , the model trained with such a biased dataset is likely to learn the bias attributes more than intrinsic attributes , the innate visual attributes that inherently define a certain class , e.g. , the wings of birds . This causes the model to learn shortcuts for classification [ 2 ] , failing to generalize on the images with no such correlations ( e.g. , birds on grounds or grass ) during the test phase . Throughout the paper , bias-aligned samples correspond to data items containing a strong correlation between bias attributes and labels ( e.g. , birds in the sky ) , while bias-conflicting samples indicate the other cases that are rarely found ( e.g. , birds on grounds ) . To tackle such a task , previous studies often define a specific bias type ( e.g. , color and texture ) in advance [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] , which enables them to design a debiasing network tailored for the predefined bias type . For example , Bahng et al . [ 6 ] leverage BagNet [ 11 ] , which has limited size * indicates equal contribution . The order of first authors was chosen by tossing a coin . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . of receptive fields , to focus on learning color and texture . However , defining a bias type in advance 1 ) limits the capability of debiasing in other bias types and 2 ) requires expensive labor to manually identify the bias type . To handle such an issue , a recent approach [ 12 ] defines a bias based on an intuitive observation that the bias attributes are often easier to learn than the intrinsic attributes for neural networks . In this regard , they re-weight bias-conflicting samples while de-emphasizing the bias-aligned ones . However , we point out that the reason behind the limited generalization capability of existing debiasing approaches lies in the significant scarcity of bias-conflicting samples compared to the bias-aligned ones in a given training set . In other words , it is challenging to learn the debiased representation from these scarce bias-conflicting samples because the models are prone to memorize ( thus being overfitted to ) these samples , failing to learn the intrinsic attributes . Therefore , we claim that a neural network can learn properly debiased representation when these data items are diversified during training . We conduct a brief experiment to demonstrate the importance of diversity in debiasing . Diversity in our work indicates the different valid realization of intrinsic attributes in a certain class ( e.g. , thick , narrow , tilted , and scribbled digit shapes in MNIST [ 13 ] ) . Our observation is that training a model with diverse bias-conflicting samples beyond a given training set is crucial for learning debiased representation ( Section 3.2 ) . In this regard , synthesizing bias-conflicting samples is one of the straightforward approaches to increase the diversity of such samples . In fact , a large amount of bias-aligned samples in a given training set already contain diverse intrinsic attributes , which can work as informative sources for increasing the diversity . However , as bias and intrinsic attributes are highly entangled in their embedding space , it is difficult to extract the intrinsic ones from these bias-aligned samples . Therefore , disentangling these correlations enables to synthesize diversified bias-conflicting samples that originate from bias-aligned samples . In this paper , we propose a novel feature augmentation approach via disentangled representation for debiasing . We first train two different encoders to embed images into the disentangled representation of their intrinsic and bias attributes . With the disentangled representation , we randomly swap the latent vectors extracted from different images , most of which are bias-aligned samples in our training set . These swapped features thus contain both bias and intrinsic attributes without the correlation between them , which , in turn , can work as augmented bias-conflicting samples in our training . These features include intrinsic features of bias-aligned ones , increasing the diversity of a given training set , especially for bias-conflicting data items . Furthermore , to enhance the quality of diversified features , we propose a scheduling strategy of feature augmentation which enables to utilize the representation disentangled to a certain degree . In summary , the main contributions of our work include : • Through our preliminary experiment , we reveal that increasing the diversity of biasconflicting samples is crucial for debiasing . • Based on such an observation , we propose a novel feature augmentation method via disentangled representation for diversifying the bias-conflicting samples . • We achieve the state-of-the-art performances in two synthetic datasets ( i.e. , Colored MNIST and Corrupted CIFAR-10 ) and one real-world dataset ( i.e. , Biased FFHQ ) against existing baselines . 2 Related Work . Debiasing predefined bias Several existing approaches mitigate the bias by pre-defining a certain bias type , either explicitly [ 3 , 4 , 5 ] or implicitly [ 6 , 7 , 8 , 9 , 10 , 14 ] . For example , Bahng et al . [ 6 ] and Wang et al . [ 7 ] design a color- and texture-oriented network to adversarially learn a debiased model against the biased one . However , as these methods still require a specific bias type such as texture in advance , they lack the general applicability to the datasets where the bias types are demanding to recognize . Instead of defining certain types of bias , recent approaches [ 12 , 15 , 16 ] rely on the straightforward assumption that networks are prone to exploit the bias when it acts as a shortcut [ 2 ] , i.e. , easy to learn in the early training phase . Nam et al . [ 12 ] emphasize the bias-conflicting samples during training by using generalized cross-entropy loss [ 17 ] . Darlow et al . [ 15 ] and Huang et al . [ 16 ] presume that high gradient of latent vectors accounts for the shortcuts that model learns . In the line with the recent studies , we tackle debiasing without pre-defining a certain bias type . Data augmentation for debiasing Geirhos et al . [ 10 ] mitigate the texture bias by utilizing additional training images with their styles being transferred by adaptive instance normalization ( AdaIN ) [ 18 ] . Minderer et al . [ 19 ] train an image-to-image translation network for removing shortcut cues in the self-supervised task . However , such image-level data augmentation is limited to resolving the predefined texture bias which can not be adopted to other general types of bias . One alternative is to exploit the latent space for data augmentation . For example , Darlow et al . [ 15 ] adversarially perturb the latent vectors corresponding to the high gradients to generate the samples against bias . Zhou et al . [ 20 ] mix the style of different source domains by AdaIN [ 18 ] to increase the domain generalization ability . Despite the effectiveness of the augmentation in the latent space , the strong unwanted correlation between bias attributes and labels prevents from obtaining the desirable intrinsic features . We resolve this issue by leveraging the disentangled representation in debiasing , which is widely used in image-to-image translation task [ 21 , 22 , 23 ] . To the best of our knowledge , no previous work in debiasing leverage this disentangled representation for the purpose of feature augmentation . For the rest of the paper , we elaborate how we perform the feature augmentation based on the disentangled representation . 3 Importance of Diversity in Debiasing . This section describes the details of a toy-set experiment in which we observe the importance of diversity in learning debiased representation . In Section 3.1 , we first introduce the two synthetic datasets , Colored MNIST and Corrupted CIFAR-10 , that we utilize for the observation . Then , we elaborate the results of the experiments in Section 3.2 . 3.1 Dataset . Colored MNIST is a modified MNIST dataset [ 13 ] with the color bias . We select ten distinct colors and inject each color on the foreground of each digit to create color bias . By adjusting the number of bias-conflicting data samples in the training set , we obtain four different datasets with the ratio of bias-conflicting samples of 0.5 % , 1 % , 2 % , and 5 % . Corrupted CIFAR-10 has ten different types of texture bias applied in CIFAR-10 [ 24 ] dataset , constructed by following the design protocol of Hendrycks and Dietterich [ 25 ] . Each class is highly correlated with a certain texture ( e.g. , frost and brightness ) . Corrupted CIFAR-10 also has four different datasets with their correlation ratios as in Colored MNIST . 3.2 Increasing diversity outperforms oversampling . To confirm the significance of diversity of bias-conflicting samples in debiasing , we train four different settings : oversampling bias-conflicting samples by 50 % in each mini-batch ( i.e. , 128 from a batch size of 256 ) , from the pool of i ) 5 % dataset and ii ) 1 % dataset , sampling bias-conflicting samples by 1 % in each mini-batch ( i.e. , 2 from a batch size of 256 ) from the pool of iii ) 5 % dataset and iv ) 1 % dataset . Oversampling provides the same amount of bias-conflicting samples as the aligned ones to the model in every training step . Bias-conflicting images sampled from the pool of 5 % dataset have more diverse appearances of bias-conflicting samples compared to those from 1 % dataset . Table 1 shows the image classification accuracy of each setting validated on the unbiased test images . Apparently , oversampling diverse bias-conflicting samples ( first row ) outperforms the other three methods . Similarly , sampling a small amount of bias-conflicting samples with the least diversity ( fourth row ) shows the lowest classification accuracy . The interesting finding is that sampling fewer but diverse conflicting samples in each mini-batch ( third row ) outperforms oversampling bias-conflicting samples with limited diversity ( second row ) . These results lead to the conclusion that the diversity of bias-conflicting samples is a more crucial factor for learning debiased representation than the ratio of sampling in the training . As the diversity is limited ( the latter case ) , the model can be easily overfitted to the given bias-conflicting samples , thus less likely to learn the generalized intrinsic attributes . With the Colored MNIST as an example , the shape of digits may vary . To be more specific , the digit shape may be thick , narrow , tilted , scribbled , and etc . If the bias-conflicting samples do not include certain visual facets ( e.g. , not including scribbled digit images ) due to the limited number of samples , the model may imperfectly learn the intrinsic attributes of digit shapes . On the other hand , in the former case ( third row ) , the model can learn multiple facets of intrinsic attributes when they are sampled from the diverse pool of datasets , resulting in learning intrinsic attributes even without oversampling the bias-conflicting images .

Summary: Dataset bias significantly degrades the generalization performance of deep neural networks. To alleviate this problem, the authors propose a novel approach for developing the debiased representation, where the model learns the disentangled representation and synthesizes diverse bias-conflicting samples via feature-level data augmentation. They demonstrate its effectiveness through extensive experiments on the image classification task.