Pseudo labeling (PL) is a wide-applied strategy to enlarge the labeled dataset by self-annotating the potential samples during the training process.

They are used to test the proposed enhancement approach iGA T. In general, ADP employs an ensemble by averaging, i.e., (C 1) ( C 1) Adversarial examples are generated to compute the losses by using the PGD attack. Our main theorem builds on a supporting Lemma 2.1. We start from the cross-entropy loss curvature measured by Eq. The above new expression of T (x) helps bound the difference between h(x) and h(x). Note that these three cases are mutually exclusive.

Guided by this theory, we propose an effective approach to improve ensemble adversarial defense, named interactive global adversarial training (iGA T).

We consider the problem of extracting a reward function by comparing a decision-making diffusion model that models low-reward behavior and one that models high-reward behavior; a setting related to inverse reinforcement learning. We first define the notion of a relative reward function of two diffusion models and show conditions under which it exists and is unique.

"Rule-of-Thumb" generation, show that our method consistently exceeds previous