Motion blur is a common cause of degraded image quality.

As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings.

Nevertheless, the existing domain adaptation methods are computationally very expensive.

By terminating BaB early, our method can also be used for efficient incomplete verification.

However, a gradient is often insufficient to reconstruct the user data without any prior knowledge. By exploiting a generative model pretrained on the data distribution, we demonstrate that data privacy can be easily breached.

We provide the supplementary material for our paper "Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning" [

The resulted CSL models provide instance-discriminative visual features that are uniformly scattered in the feature space. During deployment, the common practice is to directly fine-tune CSL models with cross-entropy, which however may not be the best strategy in practice.

To begin, we first briefly introduce some notations used throughout the appendix. The Hessian of f (Z) can be viewed as an KN KN matrix by vec-torizing the matrix Z . We will use the bilinear form for the Hessian in Appendix E. The appendix is organized as follows. In Appendix A, we discuss the relationship between this work to the previous work that beyond neural collapse. Appendix B includes the detailed description of metrics for measuring NC during network training and additional experimental results.

We also analyze several aspects of our method to demonstrate its advantages.