Preventing Latent Rehearsal Decay in Online Continual SSL with SOLAR
This paper explores Online Continual Self-Supervised Learning (OCSSL), a scenario in which models learn from continuous streams of unlabeled, non-stationary data, where methods typically employ replay and fast convergence is a central desideratum. We find that OCSSL requires particular attention to the stability-plasticity trade-off: stable methods (e.g. replay with Reservoir sampling) are able to converge faster compared to plastic ones (e.g. FIFO buffer), but incur in performance drops under certain conditions. We explain this collapse phenomenon with the Latent Rehearsal Decay hypothesis, which attributes it to latent space degradation under excessive stability of replay. We introduce ...
| Authors | Giacomo Cignoni, Simone Magistri, Andrew D. Bagdanov et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 April 2026 |
| Last updated at source | 12 April 2026 |
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