Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning

1 Introduction . Machine learning models have achieved extremely high performance in a variety of tasks and domains such as computer vision and natural language processing [ 15 , 7 , 39 ] . Recently , however , many concerns have arisen that such evaluation does not reflect the real-world performance of the model when it is deployed [ 35 , 41 ] . Among others , failing especially due to the biases existing in the dataset can lead to serious societal side effects such as prejudice or racism beyond simple algorithm failure [ 45 , 34 , 5 ] . If bias features are highly correlated with the object class in the dataset , models tend to use the bias as a cue for the prediction as they are easier to learn but enough to achieve high accuracy even though they are not actually related to the target class [ 3 , 33 ] . The failure of learning due to the bias existing in the dataset appears in various fields and tasks . To name a few , in image classification task , [ 18 , 30 ] discovered that state-of-the-art CNNs have texture biases . Visual question answering ( VQA ) model is also known to be susceptible to biases as it only uses the word occurrence in the question to generate answer [ 1 , 11 ] . For example , if most of the images of banana in the train sets are yellow , the model directly answers the question `` What is the color of the banana ? '' as `` Yellow '' , without looking at the image of green banana . Surveillance models , which can cause serious societal problems if misclassified , tend to give biased predictions toward sensitive attributes such as race [ 34 ] . Considering the importance of the problem , many approaches have been proposed to mitigate such biases in the training dataset . Depending on the presence or absence of bias information , the approaches can be divided into two categories . When the bias label is available , [ 28 , 43 ] add the bias prediction branch for the unbiased prediction . [ 40 , 10 ] directly regularize the feature embedding to be indistinguishable across the bias classes . When the bias label is unavailable , [ 11 , 3 , 8 , 20 ] design †This work was done as a student at KAIST . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . an auxiliary bias-capturing models that learn only bias features and train the main model to learn orthogonal features from those models . In this paper , we propose two powerful debiasing approaches that can be complementarily applied when the bias label is available . We first propose Bias-Contrastive ( BiasCon ) loss that extends the approaches that directly regularize the feature space by adapting the recent advances of contrastive learning in representation learning . The BiasCon loss utilizes the power of contrastive learning to promote pulling the same target class but different bias class samples closer in the feature space . We further propose the Bias-Balanced ( BiasBal ) loss that suppresses the utilization of bias features by optimizing toward the data distribution where the target-bias correlation is balanced . Each loss achieves state-of-the-art debiasing performance and shows even higher performance when they are jointly used as they give orthogonal debiasing effects . To extend to more realistic cases where the bias label is unavailable , we use the observation that the feature space of the bias-capturing model can be used in estimating whether a pair of samples have the same bias features . We thus propose Soft Bias-Contrastive ( SoftCon ) loss , which is the BiasCon loss weighted with the cosine distance between samples in the feature space of the bias-capturing model . We conduct experiments to evaluate the debiasing performance of the proposed methods . For the case where the bias label is available , we evaluate the methods on CelebA [ 31 ] and UTKFace [ 46 ] , which have biases toward sensitive attributes such as gender or race . For the case where the bias label is unavailable , we use ImageNet [ 36 ] and ImageNet-A [ 23 ] to assess whether the bias of our model has been removed . Our method improves the unbiased accuracy of previous methods by a large margin across all datasets in both with and without bias labels . Our contributions can be stated as follows : • We propose a powerful debiasing method , the BiasCon loss , that effectively adapts recent advancements of contrastive learning and the BiasBal loss that further enhances debiasing performance by optimizing the model toward distribution with uniform target-bias correlation . • We introduce the SoftCon loss , the extension of the BiasCon loss to the case where the bias label is unavailable , that utilizes the feature space of the bias-capturing model . • We show that our losses successfully improve the debiasing performance with a large gap in various real-world datasets for both bias label available/unavailable cases . 2 Related work . Bias existing in the data and the vulnerability of machine learning algorithms to such bias has been recently studied as an important problem in various domains and tasks . [ 35 , 41 ] revealed that various training sets have regularity conditions ( e.g. , objects should not be occluded ) that are unlikely to hold in practical settings , and machine learning models trained on such data fail to generalize in the absence of such conditions . More specifically , [ 23 , 44 , 30 ] showed that state-of-the-art object recognition models are biased toward backgrounds or textures associated with the object class . [ 38 ] explored that overparametrized models are prone to the spurious correlations and can have worse test errors on minority groups . Natural language processing models are also susceptible to dataset biases . Question Answering ( QA ) [ 19 , 25 , 13 , 12 ] and Visual QA [ 1 , 11 ] systems make predictions using shortcuts such as the occurrence of specific words in the question rather than the context . Even large language models tend to give biased prediction toward certain gender or race [ 10 ] . Meanwhile , the severity of such biased prediction and fairness issues of deployed models are thoroughly investigated in various domains [ 45 , 34 , 5 ] . This brings the importance and necessity of the debiasing method , and recent research has begun to proceed in two main directions depending on the presence or absence of bias labels . Debiasing when bias label is available An important line of work assumed the situation where the bias factors are categorical attributes ( e.g. , gender , race ) and the meta-information about the bias label of each training data is available . [ 43 ] suggested to use per-bias classifier head to equalize the effect of bias features . [ 45 , 32 , 14 ] proposed fairness metrics such as demographic parity ( DP ) or equalized odds ( EO ) that measure the biasedness of the model and devised algorithms that can induce the fair prediction . [ 28 ] extended the idea of the domain generalization [ 2 , 17 ] by adding a bias prediction branch to the model and train the model with an additional regularization term that induces the learned features to be invariant to the bias features . In the risk minimization perspective , [ 4 , 16 , 37 ] proposed distributionally robust optimization that aims to optimize toward the worst-case group distribution shift . [ 40 ] devised a regularization term with a triplet loss formulation to minimize the entanglement of bias features . For the pretrained language model , [ 10 ] appended additional filtering layer and corrects the sentence embedding by maximizing mutual information . We propose a novel regularization term that work upon the success of the contrastive learning framework on self-supervised learning [ 9 , 21 , 27 ] and leverages the bias label in assigning positive pair samples . Debiasing when the bias label is unavailable It is more realistic to assume that we know the form of biases that have to be removed , but no bias label is available , nor the type of bias is not limited to categorical data ( e.g. , texture bias of CNNs ) . Still , there are approaches that mitigate biases in such situations . The first line of research is to utilize the bias-capturing model [ 11 , 3 , 8 , 20 ] . The bias-capturing model is an auxiliary model designed to learn bias features . For example , texture biases can be captured by CNNs with small receptive fields [ 3 ] . In these approaches , the model is promoted to learn independent features from the bias-capturing models , thus learning debiased features . [ 33 ] further relaxed the assumption about the prior knowledge about bias based on the observation that the bias features are learned before the target features . Specific to CNNs for the image classification task , [ 18 , 30 ] removed texture bias by randomizing the texture of the training data . Our method also utilizes a bias-capturing model , but we propose a more effective way to use the model by considering the distance between training samples in the feature space of the model . 3 Method . This section begins by explaining our problem setup . Then , we propose two debiasing objectives , Bias-Contrastive ( BiasCon ) loss and Bias-Balancing ( BiasBal ) loss , that are effective in situations where the bias is categorical and the bias label is available . We further propose Soft Bias-Contrastive ( SoftCon ) loss which extends the BiasCon loss to more general and challenging situations where the bias is not limited to categorical data and the bias label is not available . 3.1 Problem definition . To formulate our problem , we define a target Y and a bias B that both affects the generation of data X . In such data , we assume that X can be divided into a signal feature Xs explaining the target Y and a bias feature Xb from B . For example , in the data for hair color classification task that is biased toward race ( that is , Y =hair and B=race ) , Xs includes brown hair feature , and Xb includes white skin feature . Now , the bias in the dataset can be understood as a high correlation between the target class and the bias feature , i.e. , the prior p ( Y |B ) is imbalanced . Then the model may ignore the true signal Xs and instead use Xb in prediction and output p ( Y |Xb ) as it can still give high accuracy . In the hair color example above , if most white people have blonde hair , the model may output blonde every time it sees the white skin color . Therefore , the biased model fails on the test data distribution with the shifted correlation between Y and B , i.e. , Ptrain ( Y , B ) ̸= Ptest ( Y , B ) . Our objective is to train the model to make a prediction using the true signal Xs only ; that is , the model takes the whole feature X and outputs p ( Y |Xs , Xb ) = p ( Y |Xs ) conditionally independent on Xb , so that the model makes indiscriminative predictions across the biases . 3.2 Bias-contrastive loss . We first consider the situation where the bias label is categorical and known for all training samples ( e.g. , gender or race information of each training sample is available ) . Here we note the fact that a biased model tends to learn features from B and make predictions using those features , hence the samples within the same bias class are close to each other in the feature space . Thus , we devise a loss to explicitly incentivise the model to pull the same target class but different bias class sample pairs closer than the other pairs . Toward this , we borrow and revise the main idea of [ 27 ] where supervised contrastive ( SupCon ) loss pulls the same class samples closer using the contrastive loss and achieves better performance than training solely with the cross-entropy loss . Specifically , we turn the SupCon loss into an effective loss for debiasing , named Bias-Contrastive ( BiasCon ) loss , that pulls samples with the same class but different bias closer while pushing the other pairs . Following the formulation of the SupCon loss , we first construct 2N size `` multiviewed batch '' by applying a pair of randomized augmentation ( e.g. , random crop , random flip ) on N data samples in a single batch : { ( xi , yi , bi ) } 2Ni=1 . We then apply the same SupCon loss but to different positive pairs ( pairs of same target class but different bias class ) and formulate the BiasCon loss as follows : LBiasCon = − 1 2N ∑ i∈Imv 1 |J ( i ) | ∑ j∈J ( i ) log exp ( zi · zj/τ ) ∑ a∈Imv\ { i } exp ( zi · za/τ ) ( 1 ) where Imv : = { 1 , ... , 2N } is the index set of multiviewed batch , J ( i ) : = { j ∈ Imv : yj = yi , bj ̸= bi } is the index set of positive samples paired with i-th sample , zi = f ( xi ) /∥f ( xi ) ∥ is a normalized feature of i-th sample extracted from f , the network up to the penultimate layer , and τ is a temperature hyperparameter . The final objective is a combination of our BiasCon loss and the standard cross-entropy ( CE ) loss as follows : LCE = − 1 N ∑ i∈I log h ( xi ) [ yi ] , ( 2 ) L = α · LCE + LBiasCon ( 3 ) where h ( x ) [ y ] = p ( y|x ) is the model prediction for class y , I : = { 1 , ... , N } is the index set of the original batch and α is a weight hyperparameter . Originally , [ 27 ] used the SupCon loss solely for the representation learning up to the penultimate layer and additionally trained the fully-connected ( FC ) layer upon the frozen representation with the CE loss . However in our case , samples with the same target and bias class are considered as a negative pairs . Therefore , it is more effective to jointly train with CE loss as it can give signals to incorporate all the same class samples . Thus , unlike [ 27 ] , we propose to use the BiasCon loss as a regularizer , as in ( 3 ) . In the Appendix D.2 , we empirically show that the joint training with CE loss achieves better performance . Another specific issue here that contrasts with the SupCon loss is that when p ( b|y ) is highly imbalanced , a single batch may not contain enough positive pairs because most of the same class samples in the batch will have the same bias features . This may result in unstable optimization . To handle this issue , for training set D = { xi } , we separately sample the mini-batch for the BiasCon loss with a modified sampling frequency Q ( i ) of i-th sample . We design Q ( i ) to oversample xi with low p ( bi|yi ) so that more positive pairs are available in the batch : Q ( i ) ∝ 1 p ( bi|yi ) if 1 p ( bi|yi ) < γ ·miny , b 1 p ( b|y ) γ ·min y , b 1 p ( b|y ) o.w. . ( 4 ) The mini-batch of size N is then sampled as { x ( j ) : x ( j ) = xi where i ∼ Q ( i ) for j = 1 , . . . , N } . Note that this formulation clips the largest possible unnormalized Q ( i ) to be γ ·minQ ( i ) so that every training data is sampled with some chance . We pre-calculate p ( b|y ) using the Y and B labels in the training set before the training . It is also instructive to note that EnD [ 40 ] has the similar motivation of entangling the features of different bias class samples . However , EnD uses the triplet loss which has a limitation that it penalties the same class samples even after the model successfully learned the target features . Experimental results in Section 4 show the huge performance gap between ours and EnD across all tasks . More detailed comparison between two methods is available in Appendix A.2 .

Summary: The authors propose three debiasing methods. First, Bias-Contrastive Loss intends to bring the feature representations for samples with the same target class but different bias labels together. Second, the authors derive a Bias-Balanced Loss, which optimizes for unbiased distribution i.e., a distribution where target class and bias labels have uniform correlations. Third, they propose Soften Bias-Contrastive loss, which does not require bias labels, but instead uses a bias-capturing model to modulate the contrastive loss. The experiments are conducted both with and without bias labels and show improvements on all of the benchmarks.