no code implementations • 9 Mar 2020 • Chenfan Zhuang, Xintong Han, Weilin Huang, Matthew R. Scott
We propose Image-Instance Full Alignment Networks (iFAN) to tackle this problem by precisely aligning feature distributions on both image and instance levels: 1) Image-level alignment: multi-scale features are roughly aligned by training adversarial domain classifiers in a hierarchically-nested fashion.
2 code implementations • ECCV 2018 • Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R. Scott, Dinglong Huang
We present a simple yet efficient approach capable of training deep neural networks on large-scale weakly-supervised web images, which are crawled raw from the Internet by using text queries, without any human annotation.
Ranked #1 on Image Classification on Clothing1M (using clean data) (using extra training data)