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Found 4 papers, 2 papers with code
Diffusion-Reward Adversarial Imitation Learning
Inspired by the recent dominance of diffusion models in generative modeling, this work proposes Diffusion-Reward Adversarial Imitation Learning (DRAIL), which integrates a diffusion model into GAIL, aiming to yield more precise and smoother rewards for policy learning.
AV-SUPERB: A Multi-Task Evaluation Benchmark for Audio-Visual Representation Models
Audio-visual representation learning aims to develop systems with human-like perception by utilizing correlation between auditory and visual information.
Diffusion Model-Augmented Behavioral Cloning
Most existing imitation learning methods that do not require interacting with environments either model the expert distribution as the conditional probability p(a|s) (e. g., behavioral cloning, BC) or the joint probability p(s, a).
Controllable User Dialogue Act Augmentation for Dialogue State Tracking
Prior work has demonstrated that data augmentation is useful for improving dialogue state tracking.
