Trainable Weight Averaging: A General Approach for Subspace Training

Training deep neural networks (DNNs) in low-dimensional subspaces is a promising direction for achieving efficient training and better generalization performance. Our previous work extracts the subspaces by performing the dimension reduction method over the training trajectory, which verifies that DNN could be well-trained in a tiny subspace. However, that method is inefficient for subspace extraction and numerically unstable, limiting its applicability to more general tasks. In this paper, we connect subspace training to weight averaging and propose \emph{Trainable Weight Averaging} (TWA), a general approach for subspace training. TWA is efficient in terms of subspace extraction and easy to use, making it a promising new optimizer for DNN's training. Our design also includes an efficient scheme that allows parallel training across multiple nodes to handle large-scale problems and evenly distribute the memory and computation burden to each node. TWA can be used for both efficient training and generalization enhancement, for different neural network architectures, and for various tasks from image classification and object detection, to neural language processing. The code of implementation is available at https://github.com/nblt/TWA, which includes extensive experiments covering various benchmark computer vision and neural language processing tasks with various architectures.
Comment: Journal version in progress. Previously accepted to ICLR 2023