41 citations · 189 across the 18 of their papers we have counts for
9 papers · 2 filters
GreedyNASv2: Greedier Search with a Greedy Path Filter
Tao Huang, Shan You, Fei Wang +4
Training a good supernet in one-shot NAS methods is difficult since the search space is usually considerably huge (e.g., ). In order to enhance the supernet's evaluation a…
Weakly Supervised Contrastive Learning
Mingkai Zheng, Fei Wang, Shan You +4
Unsupervised visual representation learning has gained much attention from the computer vision community because of the recent achievement of contrastive learning. Most of the exis…
ReSSL: Relational Self-Supervised Learning with Weak Augmentation
Mingkai Zheng, Shan You, Fei Wang +4
Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most o…
K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
Xiu Su, Shan You, Mingkai Zheng +4
In one-shot weight sharing for NAS, the weights of each operation (at each layer) are supposed to be identical for all architectures (paths) in the supernet. However, this rules ou…
ViTAS: Vision Transformer Architecture Search
Xiu Su, Shan You, Jiyang Xie +6
Vision transformers (ViTs) inherited the success of NLP but their structures have not been sufficiently investigated and optimized for visual tasks. One of the simplest solutions i…
BCNet: Searching for Network Width with Bilaterally Coupled Network
Xiu Su, Shan You, Fei Wang +3
Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constrai…
Prioritized Architecture Sampling with Monto-Carlo Tree Search
Xiu Su, Tao Huang, Yanxi Li +5
One-shot neural architecture search (NAS) methods significantly reduce the search cost by considering the whole search space as one network, which only needs to be trained once. Ho…
Locally Free Weight Sharing for Network Width Search
Xiu Su, Shan You, Tao Huang +4
Searching for network width is an effective way to slim deep neural networks with hardware budgets. With this aim, a one-shot supernet is usually leveraged as a performance evaluat…
Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search
Yibo Yang, Shan You, Hongyang Li +3
Most differentiable neural architecture search methods construct a super-net for search and derive a target-net as its sub-graph for evaluation. There exists a significant gap betw…