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Deep Semi-supervised Learning with Double-Contrast of Features and Semantics by Feng, Quan; Yao, Jiayu; Pan, Zhison; Zhou, Guojun is a scholarly article available to read on EtoBox.

What is Deep Semi-supervised Learning with Double-Contrast of Features and Semantics about?

In recent years, the field of intelligent transportation systems (ITS) has achieved remarkable success, which is mainly due to the large amount of available annotation data. However, obtaining these annotated data has to afford expensive costs in reality. Therefore, a more realistic strategy is to leverage semi-supervised learning (SSL) with a small amount of labeled data and a large amount of unlabeled data. Typically, semantic consistency regularization and the two-stage learning methods of decoupling feature extraction and classification have been proven effective. Nevertheless, representation learning only limited to semantic consistency regularization may not guarantee the separation or discriminability of representations of samples with different semantics; due to the inherent limitations of the two-stage learning methods, the extracted features may not match the specific downstream tasks. In order to deal with the above drawbacks, this paper proposes an end-to-end deep semi-supervised learning double contrast of semantic and feature, which extracts effective tasks specific discriminative features by contrasting the semantics/features of positive and negative augmented sample

Author

Feng, Quan; Yao, Jiayu; Pan, Zhison; Zhou, Guojun

Published

2022

Language

EN