Search Space Adaptation for Differentiable Neural Architecture Search in Image Classification
As deep neural networks achieve unprecedented performance in various tasks, neural architecture search (NAS), a research field for designing neural network architectures with automated processes, is actively underway. More recently, differentiable NAS has a great impact by reducing the search cost to the level of training a single network. Besides, the search space that defines candidate architectures to be searched directly affects the performance of the final architecture. In this paper, we propose an adaptation scheme of the search space by introducing a search scope. The effectiveness of proposed method is demonstrated with ProxylessNAS for the image classification task. Furthermore, we visualize the trajectory of architecture parameter updates and provide insights to improve the architecture search.
Code (0)
등록된 구현이 없습니다.
Tasks
image-classificationImage ClassificationNeural Architecture SearchMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
UNAS: Differentiable Architecture Search Meets Reinforcement Learning
Neural architecture search (NAS) aims to discover network architectures with desired properties such as high accuracy or low latency. Recently, differentiable NAS (DNAS) has demonstrated promising results while maintaini…
Neural Architecture Searchreinforcement-learningReinforcement LearningReinforcement Learning (RL)Network Architecture Search for Domain Adaptation
Deep networks have been used to learn transferable representations for domain adaptation. Existing deep domain adaptation methods systematically employ popular hand-crafted networks designed specifically for image-classi…
Domain Adaptationimage-classificationImage ClassificationNeural Architecture SearchGOLD-NAS: Gradual, One-Level, Differentiable
There has been a large literature of neural architecture search, but most existing work made use of heuristic rules that largely constrained the search flexibility. In this paper, we first relax these manually designed c…
image-classificationImage ClassificationNeural Architecture SearchDifferentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement
Recent works on One-Shot Neural Architecture Search (NAS) mostly adopt a bilevel optimization scheme to alternatively optimize the supernet weights and architecture parameters after relaxing the discrete search space int…
Bilevel OptimizationNeural Architecture SearchTND-NAS: Towards Non-differentiable Objectives in Progressive Differentiable NAS Framework
Differentiable architecture search has gradually become the mainstream research topic in the field of Neural Architecture Search (NAS) for its high efficiency compared with the early NAS methods. Recent differentiable NA…
GPUNeural Architecture Search