Semantic-Aware Auto-Encoders for Self-supervised Representation Learning

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 semantics
^1
1
Following [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.