  This paper presents Contrastive Reconstruction, ConRec - a self-supervised
learning algorithm that obtains image representations by jointly optimizing a
contrastive and a self-reconstruction loss. We showcase that state-of-the-art
contrastive learning methods (e.g. SimCLR) have shortcomings to capture
fine-grained visual features in their representations. ConRec extends the
SimCLR framework by adding (1) a self-reconstruction task and (2) an attention
mechanism within the contrastive learning task. This is accomplished by
applying a simple encoder-decoder architecture with two heads. We show that
both extensions contribute towards an improved vector representation for images
with fine-grained visual features. Combining those concepts, ConRec outperforms
SimCLR and SimCLR with Attention-Pooling on fine-grained classification
datasets.
