Long-tailed Recognition by Routing Diverse Distribution-Aware Experts

Natural data are often long-tail distributed over semantic classes . Existing recognition methods tackle this imbalanced classification by placing more emphasis on the tail data , through class re-balancing/re-weighting or ensembling over different data groups , resulting in increased tail accuracies but reduced head accuracies . We take a dynamic view of the training data and provide a principled model bias and variance analysis as the training data fluctuates : Existing long-tail classifiers invariably increase the model variance and the head-tail model bias gap remains large , due to more and larger confusion with hard negatives for the tail . We propose a new long-tailed classifier called RoutIng Diverse Experts ( RIDE ) . It reduces the model variance with multiple experts , reduces the model bias with a distribution-aware diversity loss , reduces the computational cost with a dynamic expert routing module . RIDE outperforms the state-of-the-art by 5 % to 7 % on CIFAR100-LT , ImageNet-LT and iNaturalist 2018 benchmarks . It is also a universal framework that is applicable to various backbone networks , long-tailed algorithms , and training mechanisms for consistent performance gains . Our code is available at : https : //github.com/frank-xwang/RIDE-LongTailRecognition . 1 INTRODUCTION . Real-world data are often long-tail distributed over semantic classes : A few classes contain many instances , whereas most classes contain only a few instances . Long-tailed recognition is challenging , as it needs to handle not only a multitude of small-data learning problems on the tail classes , but also extreme imbalanced classification over all the classes . There are two ways to prevent the many head instances from overwhelming the few tail instances in the classifier training objective : 1 ) class re-balancing/re-weighting which gives more importance to tail instances ( Cao et al. , 2019 ; Kang et al. , 2020 ; Liu et al. , 2019 ) , 2 ) ensembling over different data distributions which re-organizes long-tailed data into groups , trains a model per group , and then combines individual models in a multi-expert framework ( Zhou et al. , 2020 ; Xiang et al. , 2020 ) . We compare three state-of-the-art ( SOTA ) long-tail classifiers against the standard cross-entropy ( CE ) classifier : cRT and τ -norm ( Kang et al. , 2020 ) which adopt a two-stage optimization , first representation learning and then classification learning , and LDAM ( Cao et al. , 2019 ) , which is trained end-to-end with a marginal loss . In terms of the classification accuracy , a common metric for model selection on a fixed training set , Fig . 1a shows that , all these existing long-tail methods increase the overall , medium- and few-shot accuracies over CE , but decrease the many-shot accuracy . These intuitive solutions and their experimental results seem to suggest that there is a head-tail performance trade-off in long-tailed recognition . We need a principled performance analysis approach that could shed light on such a limitation if it exists and provide guidance on how to overcome it . Our insight comes from a dynamic view of the training set : It is merely a sample set of some underlying data distribution . Instead of evaluating how a long-tailed classifier performs on the fixed training set , we evaluate how it performs as the training set fluctuates according to the data distribution . Published as a conference paper at ICLR 2021Under review as a conference paper at ICLR 2021 Natural data are often long-tail distributed over semantic classes . Existing recog- nition methods tend to focus on tail performance gain , often at the expense of head performance loss from increased classifier variance . The low tail perfor- mance manifests itself in large between-class confusion and high classifier vari- ance . We aim to reduce both the bias and the variance of a long-tailed classifier by RoutIng Diverse Experts ( RIDE ) . It has three components : 1 ) a shared archi- tecture for multiple classifiers ( experts ) ; 2 ) a distribution-aware diversity loss that encourages more diverse decisions for classes with fewer training instances ; and 3 ) an expert routing module that dynamically assigns more ambiguous instances to additional experts . With on-par computational complexity , RIDE significantly outperforms the state-of-the-art methods by 5 % to 7 % on all the benchmarks including CIFAR100-LT , ImageNet-LT and iNaturalist . RIDE is also a universal framework that can be applied to different backbone networks and integrated into various re-balancing or re-weighting methods for consistent performance gains . 1 INTRODUCTION The natural data we encounter in practice often has a long tail distribution : A few classes contain many instances , while most classes contain only a few instances . Learning discrimination among them is challenging , as the few tail instances can be easily overwhelmed by many head instances . Long-tailed recognition is usually handled either by class re-balancing/re-weighting strategies giving more importance to tail instances ( Cao et al. , 2019 ; Kang et al. , 2020 ; Liu et al. , 2019 ) , or by multiexpert methods , where long-tailed data are separated into parts by their frequencies and models focusing on individual parts are combined ( Zhou et al. , 2020 ; Xiang & Ding , 2020 ) . However , all these methods generally gain on tail classes at the cost of performance loss on head classes . The state-of-the-art ( SOTA ) methods on iNaturalist ( Van Horn et al. , 2018 ) are cRT and ⌧ -norm ( Kang et al. , 2020 ) and BBN ( Zhou et al. , 2020 ) . The former belongs to the re-balancing type with a two-stage optimization for learning a good representation and classifier , whereas the latter belongs to the multi-expert type with two experts focusing on head and tail classes . We analyze the performance of a long-tail classifier in terms of bias and variance with respect to fluctuations in the training set : We randomly sample CIFAR100 ( Krizhevsky , 2009 ) according to a long-tailed distribution a few times , train a model each time , and then estimate the per-class bias and variance of the classifier . 1 Consider the training data D as a random variable . The prediction error of model h on instance x with output Y varies with the realization of D. The expected variance with respect to variable D has a well-known bias-variance decomposition : Error ( x ; h ) = E [ ( h ( x ; D ) − Y ) 2 ] = Bias ( x ; h ) + Var ance ( x ; h ) + irreducible error ( x ) . ( 1 ) For the above L2 loss on regression h ( x ) → Y , the model bias measures the accuracy of the prediction with respect to the true value , the variance measures the stability of the prediction , and the irreducible error measures the precision of the prediction and is irrelevant to the model h. Empirically , for n random sample sets of data , D ( 1 ) , . . . , D ( n ) , the k-th model trained on D ( k ) predicts y ( k ) on instance x , and collectively they have a mean prediction ym . For the L2 regression loss , the model bias is simply the L2 loss between ym and ground-truth t=E [ Y ] , whereas the model variance is the variance of y ( k ) with respect to their mean ym : L2 regression loss : L ( y ; z ) = ( y − z ) 2 ( 2 ) mean prediction : ym = 1 n n∑ k=1 y ( k ) = arg min z ED [ L ( h ( x ) ; z ) ] ( 3 ) model bias : Bias ( x ; h ) = ( ym − t ) 2 =L ( ym ; t ) ( 4 ) model variance : Variance ( x ; h ) = 1 n n∑ k=1 ( y ( k ) − ym ) 2 =ED [ L ( h ( x ) ; ym ) ] . ( 5 ) As shown on the above right , these concepts can be expressed entirely in terms of L2 loss L. We can thus extended them to classification ( Domingos , 2000 ) by replacing L with L0-1 for classification : 0-1 classification loss : L0-1 ( y ; z ) = 0 if y = z , and 1 otherwise . ( 6 ) The mean prediction ym minimizes ∑n k=1 L0-1 ( y ( k ) ; ym ) and becomes the most often or main pre- diction . The bias and variance terms become L0-1 ( ym ; t ) and 1n ∑n k=1 L0-1 ( y ( k ) ; ym ) respectively . We apply such bias and variance analysis to the CE and long-tail classifiers . We sample CIFAR100 ( Krizhevsky , 2009 ) according to a long-tail distribution multiple times . For each method , we train a model per long-tail sampled dataset and then estimate the per-class bias and variance over these multiple models on the balanced test set of CIFAR100-LT Liu et al . ( 2019 ) . Fig . 1a shows that : 1 . On the model bias : The head bias is significantly smaller than the tail bias , at 0.3 vs. 0.9 for CE . All the existing long-tail methods reduce the overall bias by primarily reducing the tail bias . However , the head-tail bias gap remains large at 0.3 vs. 0.8 . 2 . On the model variance : All the existing long-tail methods increase the model variance across all class splits , with a slight reduction in the medium-shot variance for cRT . That is , existing long-tail methods reduce the model bias for the tail at the cost of increased model variance for all the classes , and the head-tail model bias gap remains large . We conduct further statistical analysis to understand the head-tail model bias gap . We examine the largest softmax score in the other classes of { c : c 6= t } , where t is the ground-truth class of an instance . The smaller this hardest negative score is , the less the confusion , and the lower the model bias . Fig . 1b shows that there is increasingly more and larger confusion from the head to the tail . Guided by our model bias/variance and confusion pattern analysis , we propose a new long-tail classifier with four distinctive features : 1 ) It reduces the model variance for all the classes with multiple experts . 2 ) It reduces the model bias for the tail with an additional distribution-aware diversity loss . 3 ) It reduces the computational complexity that comes with multiple experts with a dynamic expert routing module which deploys another trained distinctive expert for a second ( or third , ... ) opinion only when it is called for . 4 ) The routing module and a shared architecture for experts of reduced complexity effectively cut down the computational cost of our multi-expert model , to a level that could be even lower than the commonly adopted baseline with the same backbone . Our so-called RoutIng Diverse Experts ( RIDE ) not only reduces the model variance for all the classes , but also significantly reduces the model bias for the tail classes and increases the mean accuracies for all class splits , all of which existing long-tail methods fail to accomplish . RIDE delivers 5 % ∼7 % higher accuracies than the current SOTA methods on CIFAR100-LT , ImageNet-LT ( Liu et al. , 2019 ) and iNaturalist ( Van Horn et al. , 2018 ) . RIDE is also a universal framework that can be applied to different backbone networks for improving existing long-tail algorithms such as focal loss ( Lin et al. , 2017 ) , LDAM ( Cao et al. , 2019 ) , τ -norm ( Kang et al. , 2020 ) .

Summary: The majority of feature extraction backbone is shared among different agents and the classifiers of experts are trained with both classification loss and proposed distribution-aware diversity loss. For the second stage, an expert assignment module is trained to re-weight the expert decisions. The whole paper is generally well-organized. However, there are some technical issues authors should further address: