2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVSMR-2.6
Paper Title HARDMIX: A REGULARIZATION METHOD TO MITIGATE THE LARGE SHIFT IN FEW-SHOT DOMAIN ADAPTATION
Authors Ziyun Liang, Yun Gu, Jie Yang, Shanghai Jiao Tong University, China
SessionMLR-APPL-IVSMR-2: Machine learning for image and video sensing, modeling and representation 2
LocationArea D
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video sensing, modeling, and representation
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Abstract Few-Shot Domain Adaptation aims to transfer knowledge learned from a known domain to a closely related novel domain with only a few training data available for each class. The limited number of target training data makes it challenging to bridge the domain gap and can easily lead to overfitting. In this paper, we proposed HardMix as a regularization technique which interpolates the data in feature space and assigns augmented features with 'hard' labels to eliminate the domain discrepancy. In order to generate a better decision boundary and a more compact intra-class distribution, an adaptive triplet loss is proposed to constrain the `hard' samples near the decision boundary. We demonstrate its effectiveness by comparing our results with the state-of-the-art methods on several benchmark datasets.