학술논문

Distilldarts: Network Distillation for Smoothing Gradient Distributions in Differentiable Architecture Search
Document Type
Conference
Source
2022 IEEE International Conference on Multimedia and Expo (ICME) Multimedia and Expo (ICME), 2022 IEEE International Conference on. :1-6 Jul, 2022
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Knowledge engineering
Smoothing methods
Microprocessors
Computer architecture
Benchmark testing
Robustness
Language
ISSN
1945-788X
Abstract
Recent studies show that differentiable architecture search (DARTS) suffers notable instability and collapse issue: skip-connect may gradually dominate the cell, leading to deteri-orating architectures. We conjecture that the domination of skip-connect is due to its superiority in gradient compen-sate. On this foundation, we propose a novel and stable method, called DistillDARTS, to stabilize DARTS by knowl-edge distillation and self-distillation scheme. Specifically, the distillation is able to serve as a substitute for skip-connect and smooth the back-propagated gradient distributions among layers of DARTS. By compensating gradients in shallow lay-ers, our method can relieve the dependence of gradient on skip-connect and hence mitigates the collapse issue. Exten-sive experiments on a range of benchmarks demonstrate that DistillDARTS can obtain sturdy architectures with few skip-connects without additional manual interventions, thus suc-cessfully improving the robustness of DARTS. Due to the im-proved stability, our proposed approach achieves the accuracy of 97.57% on CIFAR-10 and 75.8% on ImageNet.