Harnessing noisy Web images for deep representation

By Phong D. Vo, Alexandru-Lucian Gînsca, Hervé Le Borgne, Adrian Daniel Popescu. 2017

13 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xin He
2. Kaiyong Zhao
3. Xiaowen Chu
4. Marco F. Huber
5. Marc-André Zöller

In Computer Vision and Pattern Recognition

1. Xin He
2. Kaiyong Zhao
3. Xiaowen Chu

In Representation & Self-Supervised Learning

1. Xin He
2. Kaiyong Zhao
3. Xiaowen Chu

Citations in the corpus

• AutoML: A Survey of the State-of-the-Art (2019, background): “[65] re-rank relevant results and provide search results linearly, according to keywords.”
• Learning More Universal Representations for Transfer-Learning (2017, unknown): “Three kinds of categories are added: specific [3], [4], [37], [59] ( e.g. rot-tweiler), generic [28], [31], [49] ( e.g. dog) and noisy [25], [51].”