Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization
CoRR(2023)
摘要
The ability of machine learning systems to learn continually is hindered by
catastrophic forgetting, the tendency of neural networks to overwrite existing
knowledge when learning a new task. Continual learning methods alleviate this
problem through regularization, parameter isolation, or rehearsal, but they are
typically evaluated on benchmarks comprising only a handful of tasks. In
contrast, humans are able to learn continually in dynamic, open-world
environments, effortlessly achieving one-shot memorization of unfamiliar
objects and reliably recognizing them under various transformations. To make
progress towards closing this gap, we introduce Infinite dSprites, a
parsimonious tool for creating continual classification and disentanglement
benchmarks of arbitrary length and with full control over generative factors.
We show that over a sufficiently long time horizon, the performance of all
major types of continual learning methods deteriorates on this simple
benchmark. Thus, Infinite dSprites highlights an important aspect of continual
learning that has not received enough attention so far: given a finite
modelling capacity and an arbitrarily long learning horizon, efficient learning
requires memorizing class-specific information and accumulating knowledge about
general mechanisms. In a simple setting with direct supervision on the
generative factors, we show how learning class-agnostic transformations offers
a way to circumvent catastrophic forgetting and improve classification accuracy
over time. Our approach sets the stage for continual learning over hundreds of
tasks with explicit control over memorization and forgetting, emphasizing
open-set classification and one-shot generalization.
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