Learning a Flow to Self-Supervised Representations
Yuling Jiao, Wensen Ma, Houduo Qi, Defeng Sun
We introduce Flow-Based Distribution Matching (FBDM), which learns self-supervised representations through non-adversarial velocity regression. The encoder and auxiliary field are trained jointly by regressing analytic velocities along spherical paths from learned representations to reference targets. Both augmented views share a reference target, with a capacity limit on each reference center and an explicit view-alignment loss. A structured reference supports more components than the auxiliary flow dimension while retaining geometric separation. The auxiliary field is discarded after pretraining; only the backbone is retained downstream. On three ResNet-18 benchmarks with matched auxiliary parameter counts, FBDM provides 2.17 to 2.36 times higher per-epoch training throughput than critic-based Distribution Matching (DM), with about 0.21 GiB additional peak allocated GPU memory. Experiments show that FBDM is competitive with existing SSL methods from CIFAR to ImageNet and performs comparably to DM on their shared benchmarks. Under stated conditions, our population analysis bounds nearest-centroid classification error on the initial normalized pretraining representation by the FBDM loss.
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