27 citations · 30 across the 6 of their papers we have counts for
7 papers
Compressing Models with Few Samples: Mimicking then Replacing
Huanyu Wang, Junjie Liu, Xin Ma +3
Few-sample compression aims to compress a big redundant model into a small compact one with only few samples. If we fine-tune models with these limited few samples directly, models…
Condensation-Net: Memory-Efficient Network Architecture with Cross-Channel Pooling Layers and Virtual Feature Maps
Tse-Wei Chen, Motoki Yoshinaga, Hongxing Gao +5
"Lightweight convolutional neural networks" is an important research topic in the field of embedded vision. To implement image recognition tasks on a resource-limited hardware plat…
BAMSProd: A Step towards Generalizing the Adaptive Optimization Methods to Deep Binary Model
Junjie Liu, Dongchao Wen, Deyu Wang +4
Recent methods have significantly reduced the performance degradation of Binary Neural Networks (BNNs), but guaranteeing the effective and efficient training of BNNs is an unsolved…
QuantNet: Learning to Quantize by Learning within Fully Differentiable Framework
Junjie Liu, Dongchao Wen, Deyu Wang +4
Despite the achievements of recent binarization methods on reducing the performance degradation of Binary Neural Networks (BNNs), gradient mismatching caused by the Straight-Throug…
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers
Junjie Liu, Zhe Xu, Runbin Shi +2
We present a novel network pruning algorithm called Dynamic Sparse Training that can jointly find the optimal network parameters and sparse network structure in a unified optimizat…
DupNet: Towards Very Tiny Quantized CNN with Improved Accuracy for Face Detection
Hongxing Gao, Wei Tao, Dongchao Wen +4
Deploying deep learning based face detectors on edge devices is a challenging task due to the limited computation resources. Even though binarizing the weights of a very tiny netwo…