Variational Prototype Replays for Continual Learning

By Mengmi Zhang, Tao Wang, Joo Hwee Lim, Gabriel Kreiman, Jiashi Feng. 2019

16 citations recorded by the source corpus.

Continual learning refers to the ability to acquire and transfer knowledge without catastrophically forgetting what was previously learned. In this work, we consider \emph{few-shot} continual learning in classification tasks, and we propose a novel method, Variational Prototype Replays, that efficiently consolidates and recalls previous knowledge to avoid catastrophic forgetting. In each classification task, our method learns a set of variational prototypes with their means and variances, where embedding of the samples from the same class can be represented in a prototypical distribution and class-representative prototypes are separated apart. To alleviate catastrophic forgetting, our method replays one sample per class from previous tasks, and correspondingly matches newly predicted embeddings to their nearest class-representative prototypes stored from previous tasks. Compared with recent continual learning approaches, our method can readily adapt to new tasks with more classes without requiring the addition of new units. Furthermore, our method is more memory efficient since only class-representative prototypes with their means and variances, as well as only one sample per class from previous tasks need to be stored. Without tampering with the performance on initial tasks, our method learns novel concepts given a few training examples of each class in new tasks.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Meta & Continual Learning

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Citations in the corpus

Linear Mode Connectivity in Multitask and Continual Learning (2020, background): “Experience replay: Experience replay methods build and store a memory of the knowledge learned so far (Rebuffi et al., 2016; Lopez-Paz & Ranzato, 2017; Shin et al., 2017; Riemer et al., 2018; Rios & Itti, 2018; Zhang et al., 2019).”
Orthogonal Gradient Descent for Continual Learning (2019, background): “Shin et al. (2017); Kamra et al. (2017); Zhang et al. (2019); Rios and Itti (2018) learned a generative model to capture the data distribution of previous tasks, along with the current task’s data to train the new model so that the forgetting can be alleviated.”
Continual Learning via Bit-Level Information Preserving (2021, methodology): “The following continual image classification benchmarks are used to evaluate our method: MNIST-5 [7, 8], PMNIST [14, 29], Alternating Cifar10/100 [29], 20-Split mini-ImageNet [37, 6], Sequence of 5 tasks [8].”