Searching for TrioNet: Combining Convolution with Local and Global Self-Attention
This work addresses the need for more efficient and effective vision models by combining multiple operators, though it is incremental as it builds on existing NAS and self-attention methods.
The paper tackles the problem of exploring a novel architecture space that combines convolution with local and global self-attention operators using Neural Architecture Search, resulting in TrioNet which outperforms stand-alone models with fewer FLOPs on ImageNet and matches the best operator on small datasets.
Recently, self-attention operators have shown superior performance as a stand-alone building block for vision models. However, existing self-attention models are often hand-designed, modified from CNNs, and obtained by stacking one operator only. A wider range of architecture space which combines different self-attention operators and convolution is rarely explored. In this paper, we explore this novel architecture space with weight-sharing Neural Architecture Search (NAS) algorithms. The result architecture is named TrioNet for combining convolution, local self-attention, and global (axial) self-attention operators. In order to effectively search in this huge architecture space, we propose Hierarchical Sampling for better training of the supernet. In addition, we propose a novel weight-sharing strategy, Multi-head Sharing, specifically for multi-head self-attention operators. Our searched TrioNet that combines self-attention and convolution outperforms all stand-alone models with fewer FLOPs on ImageNet classification where self-attention performs better than convolution. Furthermore, on various small datasets, we observe inferior performance for self-attention models, but our TrioNet is still able to match the best operator, convolution in this case. Our code is available at https://github.com/phj128/TrioNet.