A.1 Statistics of correlations between different regions and the center pixel We calculate the correlations between image pixels in different log-polar regions and the center pixels on the training set of CIFAR-100. Specifically, for each pixel in each image, we divide its 11 11 neighboring area into different regions by LPSC with 3 distance levels, 8 direction levels, and a growth rate of 2. The center pixels of all areas form the center set. The pixels at the same position of all areas also form a pixel set. For each position, we calculate the correlation score between the corresponding pixel set and the center set. The correlation scores of positions in the same region of all training images are averaged to obtain the correlation score between the region and the center pixel.

The (gradient-based) bilevel programming framework is widely used in hyperparameter optimization and has achieved excellent performance empirically. Previous theoretical work mainly focuses on its optimization properties, while leaving the analysis on generalization largely open. This paper attempts to address the issue by presenting an expectation bound w.r.t. the validation set based on uniform stability. Our results can explain some mysterious behaviours of the bilevel programming in practice, for instance, overfitting to the validation set. We also present an expectation bound for the classical cross-validation algorithm. Our results suggest that gradient-based algorithms can be better than cross-validation under certain conditions in a theoretical perspective. Furthermore, we prove that regularization terms in both the outer and inner levels can relieve the overfitting problem in gradient-based algorithms. In experiments on feature learning and data reweighting for noisy labels, we corroborate our theoretical findings.

State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i.e., domain increment learning (DIL). The key idea of the paradigm is to learn prompts independently across domains with pre-trained transformers, avoiding the use of exemplars that commonly appear in conventional methods. This results in a win-win game where the prompting can achieve the best for each domain.

A Justice Ministry panel discusses how the voices of individuals should be protected under publicity and portrait rights, amid a rise in the unauthorized use of celebrities' voices by generative artificial intelligence, at the ministry in Tokyo on Friday. An expert panel under the Justice Ministry has agreed that the voices of individuals should be protected under publicity and portrait rights, amid a rise in the unauthorized use of celebrities' voices by generative artificial intelligence. The agreement was made Friday, during the first meeting of the panel on civil compensation claims related to the unauthorized use of celebrities' images and voices by generative AI. The ministry is set to compile guidelines on the scope and standards for illegal acts under current law by this summer. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Neural networks have shown great predictive power when applied to unstructured data such as images and natural languages.