6 accepted papers
We propose Noise Conditional Variational Score Distillation (NCVSD), a novel method for distilling pretrained diffusion models into generative denoisers. We achieve this by revealing that the unconditional score function implicitly characterizes the score function of denoising posterior distribution…
Learned image compression (LIC) has demonstrated superior rate-distortion (R-D) performance compared to traditional codecs, but is challenged by training inefficiency that could incur more than two weeks to train a state-of-the-art model from scratch. Existing LIC methods overlook the slow convergen…
Autofocus is a crucial component of modern digital cameras. While recent learning-based methods achieve state-of-the-art in focus prediction accuracy, they unfortunately ignore the potential focus hunting phenomenon of back-and-forth lens movement in the multi-step focusing procedure. To address thi…
Most of existing federated learning (FL) formulation is treated as a point-estimate of models, inherently prone to overfitting on scarce client-side data with overconfident decisions. Though Bayesian inference can alleviate this issue, a direct posterior inference at clients may result in biased loc…
Meta-reinforcement learning (Meta-RL), though enabling a fast adaptation to learn new skills by exploiting the common structure shared among different tasks, suffers performance degradation in the sparse-reward setting. Current hindsight-based sample transfer approaches can alleviate this issue by t…
In this paper, we propose a deep reinforcement learning (DRL)-based rate adaptation algorithm for adaptive 360-degree video streaming, which is able to maximize the quality of experience of viewers by adapting the transmitted video quality to the time-varying network conditions. Specifically, to red…