Timezone: »

 
Poster
Self-Tuning for Data-Efficient Deep Learning
Ximei Wang · Jinghan Gao · Mingsheng Long · Jianmin Wang

Tue Jul 20 09:00 AM -- 11:00 AM (PDT) @ Virtual
in Poster Session 1 »
Deep learning has made revolutionary advances to diverse applications in the presence of large-scale labeled datasets. However, it is prohibitively time-costly and labor-expensive to collect sufficient labeled data in most realistic scenarios. To mitigate the requirement for labeled data, semi-supervised learning (SSL) focuses on simultaneously exploring both labeled and unlabeled data, while transfer learning (TL) popularizes a favorable practice of fine-tuning a pre-trained model to the target data. A dilemma is thus encountered: Without a decent pre-trained model to provide an implicit regularization, SSL through self-training from scratch will be easily misled by inaccurate pseudo-labels, especially in large-sized label space; Without exploring the intrinsic structure of unlabeled data, TL through fine-tuning from limited labeled data is at risk of under-transfer caused by model shift. To escape from this dilemma, we present Self-Tuning to enable data-efficient deep learning by unifying the exploration of labeled and unlabeled data and the transfer of a pre-trained model, as well as a Pseudo Group Contrast (PGC) mechanism to mitigate the reliance on pseudo-labels and boost the tolerance to false labels. Self-Tuning outperforms its SSL and TL counterparts on five tasks by sharp margins, e.g. it doubles the accuracy of fine-tuning on Cars with $15\%$ labels.
Keywords: Deep Learning
[ Slides] 

Author Information

Ximei Wang (Tsinghua University)
Jinghan Gao (Tsinghua University)
Mingsheng Long (Tsinghua University)
Jianmin Wang (Tsinghua University)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors