Semantic-Aware Auto-Encoders for Self-supervised Representation Learning
Guangrun Wang, Yansong Tang, Liang Lin, Philip H. S. Torr · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2022
The resurgence of unsupervised learning can be attributed to the remarkable progress of self-supervised learning, which includes generative$(\mathcal{G})$and discriminative$(\mathcal{D})$models. In computer vision, the mainstream self-supervised learning algorithms are$\mathcal{D}$models. However, designing a$\mathcal{D}$model could be over-complicated; also, some studies hinted that a$\mathcal{D}$model might not be as general and interpretable as a$\mathcal{G}$model. In this paper, we switch from$\mathcal{D}$models to$\mathcal{G}$models using the classical auto-encoder$(AE)$. Note that a vanilla$\mathcal{G}$model was far less efficient than a$\mathcal{D}$model in self-supervised computer vision tasks, as it wastes model capability on overfitting semantic-agnostic high-frequency details. Inspired by perceptual learning that could use cross-view learning to perceive concepts and semantics11Following [26], we refer to semantics as visual concepts, e.g., a semantic-ware model indicates the model can perceive visual concepts, and the learned features are efficient in object recognition, detection, etc., we propose a novel$AE$that could learn semantic-aware representation via cross-view image reconstruction. We use one view of an image as the input and another view of the same image as the reconstruction target. This kind of$AE$has rarely been studied before, and the optimization is very difficult. To enhance learning ability and find a feasible solution, we propose a semantic aligner that uses geometric transformation knowledge to align the hidden code of$AE$to help optimization. These techniques significantly improve the representation learning ability of$AE$and make selfsupervised learning with$\mathcal{G}$models possible. Extensive experiments on many large-scale benchmarks (e.g., ImageNet, COCO 2017, and SYSU-30k) demonstrate the effectiveness of our methods. Code is available at https://github.com/wanggrun/Semantic-Aware-AE.