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. Previous works extract the subspaces by using random projection or performing dimensionality reduction method on the training trajectory, but these methods can be inefficient or unstable in terms of dimensionality and numerical operations. In this paper, we connect subspace training to weight averaging and propose Trainable Weight Averaging (TWA), a general approach for subspace training that generalizes the previous efforts. TWA is efficient in terms of dimensionality and also easy to use, making it a promising new method for subspace training. We further design an efficient scheme for subspace training to cope with large-scale problems, which allows parallel training across multiple nodes and evenly distributing the memory and computation burden to each node. We apply TWA to efficient neural network training and improving fine-tuning performance tasks to demonstrate the great efficiency and effectiveness of our approach. We conduct extensive experiments that cover various benchmark computer vision and neural language processing tasks with various architectures. The code of implementation is available at https://github.com/nblt/TWA.