22 citations · 35 across the 3 of their papers we have counts for
3 papers
cs.CV2022★ 22 cited
Visual Prompt Tuning for Test-time Domain Adaptation
Yunhe Gao, Xingjian Shi, Yi Zhu +5
Models should be able to adapt to unseen data during test-time to avoid performance drops caused by inevitable distribution shifts in real-world deployment scenarios. In this work,…
cs.CL2019★ 4 cited
FineText: Text Classification via Attention-based Language Model Fine-tuning
Yunzhe Tao, Saurabh Gupta, Satyapriya Krishna +3
Training deep neural networks from scratch on natural language processing (NLP) tasks requires significant amount of manually labeled text corpus and substantial time to converge,…
cs.CV2019★ 9 cited
-SNE: Domain Adaptation using Stochastic Neighborhood Embedding
Xiang Xu, Xiong Zhou, Ragav Venkatesan +2
Deep neural networks often require copious amount of labeled-data to train their scads of parameters. Training larger and deeper networks is hard without appropriate regularization…