103 citations · 196 across the 11 of their papers we have counts for
11 papers
Learning Dynamic Preference Structure Embedding From Temporal Networks
Tongya Zheng, Zunlei Feng, Yu Wang +6
The dynamics of temporal networks lie in the continuous interactions between nodes, which exhibit the dynamic node preferences with time elapsing. The challenges of mining temporal…
Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data
Gongfan Fang, Yifan Bao, Jie Song +4
Knowledge distillation~(KD) aims to craft a compact student model that imitates the behavior of a pre-trained teacher in a target domain. Prior KD approaches, despite their gratify…
Training Generative Adversarial Networks in One Stage
Chengchao Shen, Youtan Yin, Xinchao Wang +3
Generative Adversarial Networks (GANs) have demonstrated unprecedented success in various image generation tasks. The encouraging results, however, come at the price of a cumbersom…
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…
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…
Data-Free Adversarial Distillation
Gongfan Fang, Jie Song, Chengchao Shen +3
Knowledge Distillation (KD) has made remarkable progress in the last few years and become a popular paradigm for model compression and knowledge transfer. However, almost all exist…
Deep Model Transferability from Attribution Maps
Jie Song, Yixin Chen, Xinchao Wang +2
Exploring the transferability between heterogeneous tasks sheds light on their intrinsic interconnections, and consequently enables knowledge transfer from one task to another so a…
Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation
Chengchao Shen, Mengqi Xue, Xinchao Wang +3
A massive number of well-trained deep networks have been released by developers online. These networks may focus on different tasks and in many cases are optimized for different da…
Amalgamating Knowledge towards Comprehensive Classification
Chengchao Shen, Xinchao Wang, Jie Song +2
With the rapid development of deep learning, there have been an unprecedentedly large number of trained deep network models available online. Reusing such trained models can signif…
Selective Zero-Shot Classification with Augmented Attributes
Jie Song, Chengchao Shen, Jie Lei +4
In this paper, we introduce a selective zero-shot classification problem: how can the classifier avoid making dubious predictions? Existing attribute-based zero-shot classification…
Transductive Unbiased Embedding for Zero-Shot Learning
Jie Song, Chengchao Shen, Yezhou Yang +2
Most existing Zero-Shot Learning (ZSL) methods have the strong bias problem, in which instances of unseen (target) classes tend to be categorized as one of the seen (source) classe…