323 citations · 2.1k across the 141 of their papers we have counts for
12 papers · 1 filter
Progressive Network Grafting for Few-Shot Knowledge Distillation
Chengchao Shen, Xinchao Wang, Youtan Yin +3
Knowledge distillation has demonstrated encouraging performances in deep model compression. Most existing approaches, however, require massive labeled data to accomplish the knowle…
Overcoming Catastrophic Forgetting in Graph Neural Networks
Huihui Liu, Yiding Yang, Xinchao Wang
Catastrophic forgetting refers to the tendency that a neural network "forgets" the previous learned knowledge upon learning new tasks. Prior methods have been focused on overcoming…
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data
Shaoli Huang, Xinchao Wang, Dacheng Tao
Data mixing augmentation has proved effective in training deep models. Recent methods mix labels mainly based on the mixture proportion of image pixels. As the main discriminative…
Learning Propagation Rules for Attribution Map Generation
Yiding Yang, Jiayan Qiu, Mingli Song +2
Prior gradient-based attribution-map methods rely on handcrafted propagation rules for the non-linear/activation layers during the backward pass, so as to produce gradients of the…
Factorizable Graph Convolutional Networks
Yiding Yang, Zunlei Feng, Mingli Song +1
Graphs have been widely adopted to denote structural connections between entities. The relations are in many cases heterogeneous, but entangled together and denoted merely as a sin…
Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets
Weihong Ren, Xinchao Wang, Jiandong Tian +2
State-of-the-art multi-object tracking~(MOT) methods follow the tracking-by-detection paradigm, where object trajectories are obtained by associating per-frame outputs of object de…
Impression Space from Deep Template Network
Gongfan Fang, Xinchao Wang, Haofei Zhang +2
It is an innate ability for humans to imagine something only according to their impression, without having to memorize all the details of what they have seen. In this work, we woul…
Disassembling Object Representations without Labels
Zunlei Feng, Xinchao Wang, Yongming He +3
In this paper, we study a new representation-learning task, which we termed as disassembling object representations. Given an image featuring multiple objects, the goal of disassem…
Learning Oracle Attention for High-fidelity Face Completion
Tong Zhou, Changxing Ding, Shaowen Lin +2
High-fidelity face completion is a challenging task due to the rich and subtle facial textures involved. What makes it more complicated is the correlations between different facial…
Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN
Jingwen Ye, Yixin Ji, Xinchao Wang +2
Recent advances in deep learning have provided procedures for learning one network to amalgamate multiple streams of knowledge from the pre-trained Convolutional Neural Network (CN…
DEPARA: Deep Attribution Graph for Deep Knowledge Transferability
Jie Song, Yixin Chen, Jingwen Ye +4
Exploring the intrinsic interconnections between the knowledge encoded in PRe-trained Deep Neural Networks (PR-DNNs) of heterogeneous tasks sheds light on their mutual transferabil…
Distilling Knowledge from Graph Convolutional Networks
Yiding Yang, Jiayan Qiu, Mingli Song +2
Existing knowledge distillation methods focus on convolutional neural networks (CNNs), where the input samples like images lie in a grid domain, and have largely overlooked graph c…