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 article proposes a method for predicting whether a batch of data is of the same class as one of the classes already seen by a classifier or whether it contains data from another class. The idea is to then be able to incorporate this batch to the previous training set, in an unsupervised learning context. It is assumed that each batch contains data from only one class. Experiments are there to show the interest of this method for anomaly detection or incremental learning.