Problem
Research questions and friction points this paper is trying to address.
self-supervised learning
greedy layer-wise training
end-to-end backpropagation
network width
credit assignment
Increasing Width Allows Greedy Layer-wise Training to Rival End-to-End Backpropagation in Self-Supervised Learning
🤖 AI Summary
This study investigates the architectural conditions under which local greedy layer-wise training can rival end-to-end backpropagation. Leveraging self-supervised learning with convolutional neural networks, we systematically examine how network width and depth influence both greedy and end-to-end learning paradigms, providing an in-depth analysis of their representational geometry. Our findings reveal a mechanism by which increased network width effectively compensates for the limitations of restricted credit assignment inherent in local learning. Furthermore, we demonstrate that in shallower yet extremely wide architectures, greedy layer-wise training yields superior representational geometry, achieving performance that surpasses or matches that of end-to-end learning. By elucidating these dynamics, this work provides crucial theoretical foundations for the design of locally optimized neural networks.
📝 Abstract
End-to-end backpropagation has been the dominant mode of training in deep learning, allowing for the coordination of parameter updates across layers of a neural network. Prior studies have explored alternative -- and, in some cases, simpler -- training mechanisms, showing that they can sometimes achieve performance similar to backpropagation. However, the architectural conditions under which locally optimized networks, which avoid end-to-end backpropagation of error, can learn representations comparable to those learned through end-to-end training remain unclear. We aim to answer this question in the context of self-supervised learning, an important framework for large-scale pretraining in artificial intelligence. Here, we investigate how network width and depth affect the efficacy of greedy layer-wise and end-to-end self-supervised training in convolutional networks. We find that in wider networks, the benefits of end-to-end backpropagation over greedy layer-wise training shrink: in relatively shallow and very wide networks, we even observed higher performance in models trained with greedy layer-wise training. Subsequent analysis of the representations formed by these networks shows that very wide greedy-trained networks exhibit more favorable representational geometry than do networks trained end-to-end with backpropagation. This work shows that width can compensate for restricted credit assignment and identifies differences in representational geometry as a potential mechanism for their improved performance.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Greedy Layer-wise Training
Self-Supervised Learning
Network Width
Representational Geometry
Credit Assignment
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