Then, MP A denoises the learned prompts through an auto-encoding process and aligns them by maximizing the agreement of all the reconstructed prompts.

Even in large language models, negative transfer problems may still exist.

Even in large language models, negative transfer problems may still exist.

Recall measures the fraction ofcommon samples that are retrievedascorrect common class, while specificity measures thefraction ofprivatesamples thatarenotretrieved. Fig. S1(b) shows the sensitivity ofγ, where γ is the rough boundary for splitting positive and negative in adaptive filling. For the cosine similarity of two ℓ2-normalized features, the similarity value is limited from 1to1, where higher value indicates higher similarity. Suchself-supervisedlearning methods encourage the consistency between two augmentations of one image. The display images for source prototypes are chosen by finding the nearest source instance of the prototype.

To compare thequality oftargetsamples being selected fortraining, wemeasure reliability precision (howmanyofthe selected target samples were actually predicted correctly?) We report expected calibration error (ECE [7]), lower is better. We separately visualize features before and after in-domain pretraining with MAE 7and DINO 8. Wenote that these features are completely selfsupervised as the model has not seen task labels yet. Regardless, we observe a small degree of taskdiscriminativeness (examples ofthesame class areclustered together) anddomain invariance (examples of the same class but different domains are close) before additional pretraining. We now measure the degree of label overlap between ImageNet-22K and these 3 benchmarks.