2.4k citations · 2.5k across the 3 of their papers we have counts for
3 papers
cs.LG2019★ 8 cited
Semantic Bottleneck Scene Generation
Samaneh Azadi, Michael Tschannen, Eric Tzeng +3
Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the flexibility of unconditional generative models, we propose a semantic bottl…
cs.LG2019★ 168 cited
Unsupervised Domain Adaptation through Self-Supervision
Yu Sun, Eric Tzeng, Trevor Darrell +1
This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target d…
cs.CV2014★ 2.4k cited
Deep Domain Confusion: Maximizing for Domain Invariance
Eric Tzeng, Judy Hoffman, Ning Zhang +2
Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark. Fine-tuning dee…