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Deep Semi-supervised Learning with Double-Contrast of Features and Semantics | | Quan Feng
; Jiayu Yao
; Zhison Pan
; Guojun Zhou
; | | Date: |
28 Nov 2022 | | Abstract: | 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 samples
pairs. Moreover, we leverage information theory to explain the rationality of
double contrast of semantics and features and slack mutual information to
contrastive loss in a simpler way. Finally, the effectiveness of our method is
verified in benchmark datasets. | | Source: | arXiv, 2211.15671 | | Services: | Forum | Review | PDF | Favorites | |
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