5 citations · 9 across the 15 of their papers we have counts for
6 papers · 1 filter
Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling
Shayan Mohajer Hamidi, En-Hui Yang
Inverse problems exist in many disciplines of science and engineering. In computer vision, for example, tasks such as inpainting, deblurring, and super resolution can be effectivel…
Going Beyond Feature Similarity: Effective Dataset Distillation based on Class-Aware Conditional Mutual Information
Xinhao Zhong, Bin Chen, Hao Fang +3
Dataset distillation (DD) aims to minimize the time and memory consumption needed for training deep neural networks on large datasets, by creating a smaller synthetic dataset that…
JPEG Inspired Deep Learning
Ahmed H. Salamah, Kaixiang Zheng, Yiwen Liu +1
Although it is traditionally believed that lossy image compression, such as JPEG compression, has a negative impact on the performance of deep neural networks (DNNs), it is shown b…
Knowledge Distillation Based on Transformed Teacher Matching
Kaixiang Zheng, En-Hui Yang
As a technique to bridge logit matching and probability distribution matching, temperature scaling plays a pivotal role in knowledge distillation (KD). Conventionally, temperature…
AdaFed: Fair Federated Learning via Adaptive Common Descent Direction
Shayan Mohajer Hamidi, En-Hui Yang
Federated learning (FL) is a promising technology via which some edge devices/clients collaboratively train a machine learning model orchestrated by a server. Learning an unfair mo…
Bayes Conditional Distribution Estimation for Knowledge Distillation Based on Conditional Mutual Information
Linfeng Ye, Shayan Mohajer Hamidi, Renhao Tan +1
It is believed that in knowledge distillation (KD), the role of the teacher is to provide an estimate for the unknown Bayes conditional probability distribution (BCPD) to be used i…