103 citations · 623 across the 53 of their papers we have counts for
9 papers · 1 filter
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…
Hearing Lips: Improving Lip Reading by Distilling Speech Recognizers
Ya Zhao, Rui Xu, Xinchao Wang +3
Lip reading has witnessed unparalleled development in recent years thanks to deep learning and the availability of large-scale datasets. Despite the encouraging results achieved, t…
Dynamic Instance Normalization for Arbitrary Style Transfer
Yongcheng Jing, Xiao Liu, Yukang Ding +4
Prior normalization methods rely on affine transformations to produce arbitrary image style transfers, of which the parameters are computed in a pre-defined way. Such manually-defi…
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…
A Cascade Sequence-to-Sequence Model for Chinese Mandarin Lip Reading
Ya Zhao, Rui Xu, Mingli Song
Lip reading aims at decoding texts from the movement of a speaker's mouth. In recent years, lip reading methods have made great progress for English, at both word-level and sentenc…