While the toy and scRNA-seq datasets do not have a split, we used the training and test set of CIFAR-10 jointly for the unsupervised UMAP dimensionreduction.

This question is particularly relevant - and has received significant attention - in the backdrop of multi-agentlearningingames.

Rightbarswith : Ontheotherhand, the proposed counterpart(10) does not explode. Data augmentation is a pre-defined stochastic function such asacomposition ofthe horizontal flipping and cropping.

Instance discriminative self-supervised representation learning has been attracted attention thanks to its unsupervised nature and informative feature representation for downstream tasks. In practice, it commonly uses a larger number of negative samples than the number of supervised classes. However, there is an inconsistency in the existing analysis; theoretically, a large number of negative samples degrade classification performance on a downstream supervised task, while empirically, they improve the performance. We provide a novel framework to analyze this empirical result regarding negative samples using the coupon collector's problem. Our bound can implicitly incorporate the supervised loss of the downstream task in the self-supervised loss by increasing the number of negative samples. We confirm that our proposed analysis holds on real-world benchmark datasets.

Additionally, prompt-based FT with the PCP outperforms state-of-the-art semi-supervised approaches with greater simplicity, eliminating the need for an iterative process and extra data augmentation. Our further analysis explores the performance lower bound of the PCP and reveals that the advantages of PCP persist across different sizes of models and datasets.