SVD-NAS: Coupling Low-Rank Approximation and Neural Architecture Search
This work addresses the need for efficient neural network compression without data access, offering a novel framework that improves accuracy and reduces computational costs, though it is incremental in combining existing domains.
The paper tackled the problem of compressing pre-trained deep neural networks by coupling low-rank approximation with neural architecture search, resulting in 2.06-12.85 percentage points higher accuracy on ImageNet compared to state-of-the-art methods under data-limited settings.
The task of compressing pre-trained Deep Neural Networks has attracted wide interest of the research community due to its great benefits in freeing practitioners from data access requirements. In this domain, low-rank approximation is a promising method, but existing solutions considered a restricted number of design choices and failed to efficiently explore the design space, which lead to severe accuracy degradation and limited compression ratio achieved. To address the above limitations, this work proposes the SVD-NAS framework that couples the domains of low-rank approximation and neural architecture search. SVD-NAS generalises and expands the design choices of previous works by introducing the Low-Rank architecture space, LR-space, which is a more fine-grained design space of low-rank approximation. Afterwards, this work proposes a gradient-descent-based search for efficiently traversing the LR-space. This finer and more thorough exploration of the possible design choices results in improved accuracy as well as reduction in parameters, FLOPS, and latency of a CNN model. Results demonstrate that the SVD-NAS achieves 2.06-12.85pp higher accuracy on ImageNet than state-of-the-art methods under the data-limited problem setting. SVD-NAS is open-sourced at https://github.com/Yu-Zhewen/SVD-NAS.