8 accepted papers
Blending multiple convolutional kernels is proved advantageous in neural architecture design. However, current two-stage neural architecture search methods are mainly limited to single-path search spaces. How to efficiently search models of multi-path structures remains a difficult problem. In this…
Based on the weight-sharing mechanism, one-shot NAS methods train a supernet and then inherit the pre-trained weights to evaluate sub-models, largely reducing the search cost. However, several works have pointed out that the shared weights suffer from different gradient descent directions during tra…
Time-consuming performance evaluation is the bottleneck of traditional Neural Architecture Search (NAS) methods. Predictor-based NAS can speed up performance evaluation by directly predicting performance, rather than training a large number of sub-models and then validating their performance. Most p…
Neural Architecture Search (NAS) aims to automatically find optimal neural network architectures in an efficient way. Zero-Shot NAS is a promising technique that leverages proxies to predict the accuracy of candidate architectures without any training. However, we have observed that most existing pr…
Micro- and macro-architecture search have emerged as two popular NAS paradigms recently. Existing methods leverage different search strategies for searching micro- and macro- architectures. When using architecture parameters to search for micro-structure such as normal cell and reduction cell, the a…
With the success of Transformers in the computer vision field, the automated design of vision Transformers has attracted significant attention. Recently, MetaFormer found that simple average pooling can achieve impressive performance, which naturally raises the question of how to design a search spa…
Despite the fast development of differentiable architecture search (DARTS), it suffers from a standing instability issue regarding searching performance, which extremely limits its application. Existing robustifying methods draw clues from the outcome instead of finding out the causing factor. Vario…
Predictor-based Neural Architecture Search (NAS) continues to be an important topic because it aims to mitigate the time-consuming search procedure of traditional NAS methods. A promising performance predictor determines the quality of final searched models in predictor-based NAS methods. Most exist…