Putting An End to End-to-End: Gradient-Isolated Learning of Representations

Understand

We propose a novel deep learning method for local self-supervised representation learning that does not require labels nor end-to-end backpropagation but exploits the natural order in data instead.

• Inspired by the observation that biological neural networks appear to learn without backpropagating a global error signal, we split a deep neural network into a stack of gradient-isolated modules.
• Each module is trained to maximally preserve the information of its inputs using the InfoNCE bound from Oord et al.
• [2018].