Sakana AI Proposes DiffusionBlocks: a Block-wise Training Framework That Converts Residual Networks into Independently Trainable Denoising Modules
Researchers from Sakana AI and the University of Tokyo suggest DiffusionBlocks. It trains transformer-based networks one block at a time. Training reminiscence is lowered by a issue of B, the place B is the variety of blocks. Performance is maintained throughout various architectures. The Memory Problem in Neural Network Training End-to-end backpropagation requires storing intermediate…
