539 citations · 2k across the 53 of their papers we have counts for
6 papers · 1 filter
Neural Network Pruning with Residual-Connections and Limited-Data
Jian-Hao Luo, Jianxin Wu
Filter level pruning is an effective method to accelerate the inference speed of deep CNN models. Although numerous pruning algorithms have been proposed, there are still two open…
Neural Random Subspace
Yun-Hao Cao, Jianxin Wu, Hanchen Wang +1
The random subspace method, known as the pillar of random forests, is good at making precise and robust predictions. However, there is not a straightforward way yet to combine it w…
Repetitive Reprediction Deep Decipher for Semi-Supervised Learning
Guo-Hua Wang, Jianxin Wu
Most recent semi-supervised deep learning (deep SSL) methods used a similar paradigm: use network predictions to update pseudo-labels and use pseudo-labels to update network parame…
Deep Learning for Fine-Grained Image Analysis: A Survey
Xiu-Shen Wei, Jianxin Wu, Quan Cui
Computer vision (CV) is the process of using machines to understand and analyze imagery, which is an integral branch of artificial intelligence. Among various research areas of CV,…
Towards Real-Time Action Recognition on Mobile Devices Using Deep Models
Chen-Lin Zhang, Xin-Xin Liu, Jianxin Wu
Action recognition is a vital task in computer vision, and many methods are developed to push it to the limit. However, current action recognition models have huge computational co…
Probabilistic End-to-end Noise Correction for Learning with Noisy Labels
Kun Yi, Jianxin Wu
Deep learning has achieved excellent performance in various computer vision tasks, but requires a lot of training examples with clean labels. It is easy to collect a dataset with n…