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Siamese-network-based self-supervised learning (SSL) suffers from slowconvergence and instability in training. To alleviate this, we propose aframework to exploit intermediate self-supervisions in each stage of deep nets,called the Ladder Siamese Network. Our self-supervised losses encourage theintermediate layers to be consistent with different data augmentations tosingle samples, which facilitates training progress and enhances thediscriminative ability of the intermediate layers themselves. While someexisting work has already utilized multi-level self supervisions in SSL, oursis different in that 1) we reveal its usefulness with non-contrastive Siameseframeworks in both theoretical and empirical viewpoints, and 2) ours improvesimage-level classification, instance-level detection, and pixel-levelsegmentation simultaneously. Experiments show that the proposed framework canimprove BYOL baselines by 1.0% points in ImageNet linear classification, 1.2%points in COCO detection, and 3.1% points in PASCAL VOC segmentation. Incomparison with the state-of-the-art methods, our Ladder-based model achievescompetitive and balanced performances in all tested benchmarks without causinglarge degradation in one.

Ladder Siamese Network: a Method and Insights for Multi-level Self-Supervised Learning