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In recent years, the field of intelligent transportation systems (ITS) hasachieved remarkable success, which is mainly due to the large amount ofavailable annotation data. However, obtaining these annotated data has toafford expensive costs in reality. Therefore, a more realistic strategy is toleverage semi-supervised learning (SSL) with a small amount of labeled data anda large amount of unlabeled data. Typically, semantic consistencyregularization and the two-stage learning methods of decoupling featureextraction and classification have been proven effective. Nevertheless,representation learning only limited to semantic consistency regularization maynot guarantee the separation or discriminability of representations of sampleswith different semantics; due to the inherent limitations of the two-stagelearning methods, the extracted features may not match the specific downstreamtasks. In order to deal with the above drawbacks, this paper proposes anend-to-end deep semi-supervised learning double contrast of semantic andfeature, which extracts effective tasks specific discriminative features bycontrasting the semantics/features of positive and negative augmented samplespairs. Moreover, we leverage information theory to explain the rationality ofdouble contrast of semantics and features and slack mutual information tocontrastive loss in a simpler way. Finally, the effectiveness of our method isverified in benchmark datasets.

Deep Semi-supervised Learning with Double-Contrast of Features and Semantics