[Adapt] [Seminar] How to mitigate the requirement for labeled data? Self-Tuning for Data-Efficient Deep Learning.

[Xiujie Song]

Hi Adapters,

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. How to mitigate the requirement for labeled data is one important problem.

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, some researchers 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. 

In this talk, I will introduce this paper, Self-Tuning for Data-Efficient Deep Learning, to you, which is published in ICML 2021. Hope you enjoy it~

For our online group seminar tomorrow, I prepared a Tencent meeting room:

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边缘 邀请您参加腾讯会议
会议主题：边缘预定的会议
会议时间：2022/03/23 16:00-18:00 (GMT+08:00) 中国标准时间 - 北京

点击链接入会，或添加至会议列表：
https://meeting.tencent.com/dm/ASSrQvyjywtd

#腾讯会议：498-963-814

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+8675536550000,,498963814# (中国大陆)
+85230018898,,,2,498963814# (中国香港)

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Time: Wed 4:00pm

Venue: Tencent meeting 498-963-814

Best Wishes,
Xiujie Song