34 citations · 122 across the 30 of their papers we have counts for
6 papers · 1 filter
Augmented Shortcuts for Vision Transformers
Yehui Tang, Kai Han, Chang Xu +4
Transformer models have achieved great progress on computer vision tasks recently. The rapid development of vision transformers is mainly contributed by their high representation a…
Manifold Regularized Dynamic Network Pruning
Yehui Tang, Yunhe Wang, Yixing Xu +4
Neural network pruning is an essential approach for reducing the computational complexity of deep models so that they can be well deployed on resource-limited devices. Compared wit…
Pre-Trained Image Processing Transformer
Hanting Chen, Yunhe Wang, Tianyu Guo +7
As the computing power of modern hardware is increasing strongly, pre-trained deep learning models (e.g., BERT, GPT-3) learned on large-scale datasets have shown their effectivenes…
SCOP: Scientific Control for Reliable Neural Network Pruning
Yehui Tang, Yunhe Wang, Yixing Xu +4
This paper proposes a reliable neural network pruning algorithm by setting up a scientific control. Existing pruning methods have developed various hypotheses to approximate the im…
Searching for Low-Bit Weights in Quantized Neural Networks
Zhaohui Yang, Yunhe Wang, Kai Han +4
Quantized neural networks with low-bit weights and activations are attractive for developing AI accelerators. However, the quantization functions used in most conventional quantiza…
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
Zhaohui Yang, Yunhe Wang, Xinghao Chen +6
Neural Architecture Search (NAS) refers to automatically design the architecture. We propose an hourglass-inspired approach (HourNAS) for this problem that is motivated by the fact…