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: The paper presents a way to generate bias-conflicting samples at feature level, which help de-bias the model. For this, the authors first propose learning two separate (disentangled) representations: a) representation of implicit attributes (i.e., necessary to predict the target) learned through the normal cross-entropy loss and b) representation of biased attributes learned through generalized cross entropy loss (also used by LfF for bias amplification). The empirical results demonstrate that generating such samples outperforms oversampling/reweighting.