Edition 077 · work · Munia; Nusrat; Ward; Tyler; Nayla; Nishat; Massey; Matthew A; Imran; Abdullah-Al-Zubaer
Self-supervised learning needs a fresh approach
Current pretraining and finetuning stages for self-supervised learning are inefficient and limit true potential.
FO Take · Score 85
The two-stage SSL paradigm is a relic. Pretraining on unlabelled data, then finetuning on labelled sets, wastes resources and time. We must move to joint training, integrating supervision from the outset to unlock genuine AI efficiency. Staged learning offers diminishing returns. Why are we still debating this obvious shift?
The strongest counter
Two-stage SSL allows for greater flexibility and adaptation to diverse downstream tasks. It fosters generalisation before specialisation, a crucial learning dynamic.
