4.4k citations · 7k across the 47 of their papers we have counts for
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A Survey on Visual Transformer
Kai Han, Yunhe Wang, Hanting Chen +10
Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism. Thanks to its strong represen…
Dynamic Feature Pyramid Networks for Object Detection
Mingjian Zhu, Kai Han, Changbin Yu +1
Feature pyramid network (FPN) is a critical component in modern object detection frameworks. The performance gain in most of the existing FPN variants is mainly attributed to the i…
Training Binary Neural Networks through Learning with Noisy Supervision
Kai Han, Yunhe Wang, Yixing Xu +3
This paper formalizes the binarization operations over neural networks from a learning perspective. In contrast to classical hand crafted rules (\eg hard thresholding) to binarize…
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets
Kai Han, Yunhe Wang, Qiulin Zhang +3
To obtain excellent deep neural architectures, a series of techniques are carefully designed in EfficientNets. The giant formula for simultaneously enlarging the resolution, depth…
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…
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection
Jianyuan Guo, Kai Han, Yunhe Wang +5
Neural Architecture Search (NAS) has achieved great success in image classification task. Some recent works have managed to explore the automatic design of efficient backbone or fe…
Widening and Squeezing: Towards Accurate and Efficient QNNs
Chuanjian Liu, Kai Han, Yunhe Wang +3
Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than th…