41 citations · 107 across the 10 of their papers we have counts for
10 papers · 1 filter
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…
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…