Auto Seed Vl2 -

[3] Zhou, K., et al. (2022). Learning to prompt for vision-language models. IJCV.

The consistency loss and gradient-conditioned generation are crucial. Seed pruning is memory-efficient without hurting accuracy. We measure FWT: performance on task ( t ) after training on tasks ( 1..t-1 ). Auto-Seed VL2 achieves positive forward transfer (FWT = +4.1%) on VL-CL, meaning seeds from earlier tasks help learn new tasks. ER-VLM shows near-zero FWT; generative replay shows negative transfer due to noisy synthetic images. 7. Analysis and Discussion What do generated seeds encode? We project seeds into CLIP space and compare to real class means. The cosine similarity is 0.89 ± 0.05, indicating faithful representation. However, seeds are more “regularized” – they have lower variance along task-irrelevant directions. auto seed vl2

[6] von Oswald, J., et al. (2020). Continual learning with hypernetworks. ICLR. [3] Zhou, K