Unsupervised Class-Incremental Learning through Confusion

1 INTRODUCTION . The development of continually learning systems remains to be a major obstacle in the field of artificial intelligence . The primary challenge is to mitigate catastrophic forgetting : learning new tasks while maintaining the ability to perform old ones . This domain of research is often referred to as Continual Learning , Lifelong Learning , Sequential Learning , or Incremental Learning : each with subtleties in the learning environment and training process , but most with the use of supervision ( De Lange et al . ( 2020 ) ) . Recently , Stojanov et al . ( 2019 ) introduced a novel unsupervised class-incremental learning problem motivated by the desire to simulate how children ’ s play behaviors support their ability to learn object models ( IOLfCV ) . Here , sequential tasks take the form of exposures . Each exposure is comprised of a set of images that pertains to a single class that is hidden from the learner . Exposure boundaries , the transition from one exposure to the next , are known . The model trained in this setting , is analogous to a young child that has been placed in a playpen with a set of new toys . The child steadily gathers information over time by picking up , examining , and putting down new/old objects continuously . Similar to how the child does not have any guidance to the toys they will examine , the agent does not have access to the exposure ’ s identity during training . To learn in the unsupervised class-incremental setting , an agent must conduct two procedures successfully . Given a new learning exposure , the key step is to perform novelty detection : to identify whether an exposure corresponds to a class that has been learned . If the agent determines that an exposure is familiar , the second step is to identify its label such that the exposure can be leveraged to update the model . Both procedures must be performed reliably . Otherwise , the novelty detection mistakes will result in label noise that distorts the learned model , increasing the likelihood of subsequent mistakes . Deep neural networks are known to make overconfident decisions for anomalous data distributions that were not seen during training ( Hendrycks & Gimpel ( 2016 ) ) . To address this problem , research related to out-of-distribution ( OOD ) detection have utilized supervised methods ( Liang et al . ( 2017 ) ; Alemi et al . ( 2018 ) ) and unsupervised methods ( Choi & Jang ( 2018 ) ; Hendrycks et al . ( 2018 ) ; Serrà et al . ( 2019 ) ) . Works related to open set recognition have also addressed the OOD problem by applying distance-based thresholds computed from known class scores ( Scheirer et al . ( 2012 ; 2014 ) ) . The work by Stojanov et al . ( 2019 ) applies a similar method to the unsupervised incremental setting by computing class features produced from a set of supervised samples . In contrast , we propose a model , Incremental Learning by Accuracy Performance ( iLAP ) , that determines class novelty and identity by considering performance changes of previously learned tasks when an incoming set of exposure images are trained under a new label . Instead of using a distance-based metric , our novelty detection threshold relies on the percentage of accuracy that was maintained by performing a model update using the incoming exposure . This poses several practical advantages : First , the threshold value does not rely on supervised samples and is more intuitive ( Section 3.3 ) . Second , the performance of our method is independent of the sequence of the incoming exposure classes ( Section 5.2 ) . Finally , the model is able to distinguish between similar classes more reliably ( Section 5.3 ) . From our experiments , we demonstrate that the confusion resulting from training with label ambiguity results in a more reliable signal for novelty detection in comparison to previous methods . We demonstrate that our technique is more robust and results in substantial performance gains in comparison to various baselines . Furthermore , despite the absence of labels , our model was able to perform similarly to supervised models under several benchmarks . In summary , this work provides three contributions : • We present a novel framework , iLAP , that achieves learning in the unsupervised classincremental environment where the exposure identities are unknown . • We demonstrate that by including a class-imbalance technique , our unsupervised method is able to closely match supervised performance for several image classification benchmarks trained in the incremental setting . • We identify failure cases that are overlooked by traditional OOD methods that leverage distance-based thresholds . 2 RELATED WORKS . Introduced by Stojanov et al . ( 2019 ) , the unsupervised class-incremental setting contains a set of sequential tasks that are single-class exposures ; classes pertaining to the exposures may repeat and are unknown . This is not to be mistaken with unsupervised continual learning ( UCL ) where task boundaries and task identities are unavailable ( Lee et al . ( 2020 ) ; Smith & Dovrolis ( 2019 ) ; Rao et al . ( 2019 ) ) . Our work presents an agent that is able to leverage the boundary information from the unsupervised class-incremental environment to achieve performances that are close to models trained under supervision . 2.1 CONTINUAL LEARNING/INCREMENTAL LEARNING . Prior works in this field primarily aim to improve a model ’ s ability to retain information while incorporating new tasks ( Goodfellow et al . ( 2013 ) ; Parisi et al . ( 2019 ) ; Rebuffi et al . ( 2017 ) ; LopezPaz & Ranzato ( 2017 ) ; Aljundi et al . ( 2018 ) ; Castro et al . ( 2018 ) ) . Typically , these models reside in learning settings where both task labels and task boundaries are available . Methods include replay techniques , the usage of artifacts and generated samples to refresh a model ’ s memory ( Kamra et al . ( 2017 ) ; Wu et al . ( 2018 ) ; Rolnick et al . ( 2019 ) ; Shin et al . ( 2017 ) ; Wu et al . ( 2019 ) ) , and regularization-based practices , the identification and preservation of weights that are crucial for the performance of specific tasks ( Kirkpatrick et al . ( 2017 ) ; Zenke et al . ( 2017 ) ; Yoon et al . ( 2017 ) ) . In contrast to prior works , our method addresses incremental learning in a setting where exposure labels are unavailable . 2.2 UNSUPERVISED CONTINUAL LEARNING . Recently , a series of works tackle the UCL problem where task boundaries and task identities are unknown . Smith & Dovrolis ( 2019 ) performs novelty detection by analyzing an input image through a series of receptive fields to determine if an input patch is an outlier . Meanwhile , CURL proposes a method to learn class-discriminative representations through a set of shared parameters ( Rao et al . ( 2019 ) ) . CN-DPM , introduces an expansion-based approach that utilizes a mixture of experts to learn feature representations ( Lee et al . ( 2020 ) ) . Although CN-DPM performs in a task-free setting , incoming tasks are multi-class and individual class labels are provided . This supervision is required to train the existing experts and determine when a new one is needed . While boundary information is not required for these works , the performances are far below supervised baselines ( 77.7 % on and MNIST 13.2 % Omniglot ) ( Rao et al . ( 2019 ) ) . 2.3 OUT-OF-DISTRIBUTION DETECTION . This ongoing area of research aims to detect outliers in training and testing data . Current approaches can be largely categorized by statistical , distance-based , and deep learning methods ( Eskin ( 2000 ) ; Yamanishi et al . ( 2004 ) ; Knorr et al . ( 2000 ) ; Hautamaki et al . ( 2004 ) ; Sabokrou et al . ( 2018 ) ; Kliger & Fleishman ( 2018 ) ) . Recent techniques involve using a threshold to determine class novelty from network confidence values ( Hendrycks & Gimpel ( 2016 ) ) . ODIN uses input perturbations to increase softmax scores for neural networks to distinguish in-distribution images from out-of-distribution images ( Liang et al . ( 2017 ) ) . DeVries & Taylor ( 2018 ) incorporates a confidence branch to obtain out-of-distribution estimations . Our method ( iLAP ) is the first to incorporate a threshold value that is dependent on class-accuracy changes caused by data poisoning . 3 APPROACH . In this section , we provide an overview of our method . We begin by identifying the learning setting , followed by details of our training process . Finally , insights for choosing threshold values are provided . 3.1 SETTING . In the unsupervised class-incremental setting , a learner L perceives an input stream of exposures denoted as E1 , E2 , ... , En . Each exposure contains a set of images , Ei = { ei1 , ei2 , ... , eini } , e i j ∈ RC×H×W , where C , H , and W are the number of channels , height , and width of the input image respectively . Each exposure belongs to a single class yi ∈ N , which has been sampled from class distribution P ( C ) . For each Ei , L does not have knowledge of the true class label yi . Two exemplars , Ptrain = ( P 1train , P 2train , ... , P K̂train ) and Pval = ( P 1val , P 2val , ... , P K̂val ) , are maintained at all times , where K̂ denotes the total number of classes L has currently determined . The exemplars are used to store samples from the exposure for replay and accuracy assessment . The sizes of both exemplars , ∣∣P itrain∣∣ and ∣∣P ival∣∣ , are bounded per class . 3.2 DETECTION TRAINING . For each incoming exposure , the model is tasked to determine whether the class associated with the exposure was learned previously . Our solution is to perform a model update by treating the incoming exposure as a new class , we coin this technique detection training . Under the circumstances that the exposure class is repeated , the performance for the previously learned class would suffer drastically after training . The reason for this behavior is because the model has associated two different labels to a similar class distribution . During detection training , L̂ , a copy of L is produced . The incoming exposure is assigned with label K̂ + 1 . Train-validation split is performed on the incoming exposure to obtain Etrain and Eval , and are aggregated with exemplars Ptrain and Pval respectively . The combined samples are used to train L̂ via validation-based early stopping . We denote the vector { ∆ŷ } ŷ∈ [ K̂ ] to represent the percentage decrease of the class accuracies ( computed using Pval ) before and after the update . If max ( { ∆ŷ } ) exceeds a threshold , θ , the incoming exposure is likely to have been learned by L. In this case , the correct identity pertaining to the exposure is arg maxŷ∈ [ K̂ ] ∆ŷ . Otherwise , if θ is unsatisfied , K̂ + 1 is the appropriate label for the new class . 3.3 CLASS-IMBALANCE FOR DETECTION TRAINING . Introducing a class-imbalance during detection training creates a more distinct decision boundary by exacerbating the class-accuracy drop for repeated exposures . Consider a theoretical case where an optimal model has learned K̂ classes . The incoming exposure , Ei , contains a distribution that is equal to that of some previously learned class ŷi . If the model were updated with equal samples of Ei labeled K̂ + 1 , and P ŷitrain labeled ŷi , the accuracy for class ŷi would become ambiguous during validation ( ŷi≈ 50 % , K̂ + 1≈ 50 % ) . However , if the model were updated with a greater sample of K̂ + 1 labels , the accuracy drop for class ŷi would be considerably larger because K̂ + 1 would be favored during inference . L̂ has the option to use an imbalanced dataset during detection training where a fraction of the size for each class from Ptrain are used in comparison to the size of Etrain . Let P isampled ⊂ P itrain , the class-imbalance ratio λ is : λ = 1− |P isampled| |Etrain| ( 1 ) In Figure 2 , we use Fashion-MNIST ( Xiao et al . ( 2017 ) ) and Imagenette ( Howard ) to obtain a general value for λ and θ for our experiments with other datasets . Performance drops are tracked for the repeated class and non-repeated classes during detection training with various values of λ . The goal is to maximize the performance loss for the repeated class while maintaining the performance for the other classes . In Section 4 a θ value of 0.6 and a λ value of 0.5 are used . λ is determined by the point of the maximum distance between the two accuracy curves ; θ , the expected accuracy drop for a repeated exposure , is the mean of the two values at λ Figure 2 .

Summary: This paper proposes to tackle the problem of unsupervised class-incremental learning, where the training data is composed of a sequence of "exposures". Each exposure is comprised of a set of images that pertains to a single class, where the class label is unknown while the boundaries between exposures are known. The key difficulty in such unsupervised class-incremental learning is to determine whether an arriving exposure belongs to what the classification model $L$ has learnt previously or is a novel one, thus relating to the problem of novelty detection. The proposed method address the novelty detection by an interesting idea: they always treat the current exposure as a novel class and use it to train the copy of classification model $\hat{L}$ together with the training exemplars of previously-learnt classes, if the current exposure actually belongs to one of the previous-learnt classes, the confusion occurs to make the classification accuracy significantly decrease (over a threshold) on that specific class, where the accuracy is computed based on the validation exemplars. Moreover, a technique of introducing class-imbalance into such confusion-based novelty detection is proposed and helps to boost the robustness of novelty detection.