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Defining Benchmarks for Continual Few-Shot Learning
Massimiliano Patacchiola

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In recent years there has been substantial progress in few-shot learning, where a model is trained on a small labeled dataset related to a specific task, and in continual learning, where a model has to retain knowledge acquired on a sequence of datasets. However, the field has still to frame a suite of benchmarks for the hybrid setting combining these two paradigms, where a model is trained on several sequential few-shot tasks, and then tested on a validation set stemming from all those tasks. In this paper we propose such a setting, naming it Continual Few-Shot Learning (CFSL). We first define a theoretical framework for CFSL, then we propose a range of flexible benchmarks to unify the evaluation criteria. As part of the benchmark, we introduce a compact variant of ImageNet, called SlimageNet64, which retains all original 1000 classes but only contains 200 instances of each one (a total of 200K data-points) downscaled to 64 by 64 pixels. We provide baselines for the proposed benchmarks using a number of popular few-shot and continual learning methods, exposing previously unknown strengths and weaknesses of those algorithms. The dataloader and dataset will be released with an open-source license.

Author Information

Massimiliano Patacchiola (University of Edinburgh)

Massimiliano is a postdoctoral researcher at the University of Cambridge in the Machine Learning Group. He is interested in efficient learning (few-shot, self-supervised, meta-learning), Bayesian methods (Gaussian processes), and reinforcement learning. Previously he has been a postdoctoral researcher at the University of Edinburgh and an intern in the Camera Platform team at Snapchat.

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