11 papers
Learned JPEG Compression for DNN Vision
Kaixiang Zheng, Ahmed H. Salamah, Siyu Chen +1
JPEG, a lossy image compression technique designed for human viewers, has maintained its dominance for decades. However, in the era of artificial intelligence (AI), a substantial p…
Brain4FMs: A Benchmark of Foundation Models for Electrical Brain Signal
Fanqi Shen, Enhong Yang, Jiahe Li +5
Brain Foundation Models (BFMs) are transforming neuroscience by enabling scalable and transferable learning from neural signals, advancing both clinical diagnostics and cutting-edg…
Normalized Conditional Mutual Information Surrogate Loss for Deep Neural Classifiers
Linfeng Ye, Zhixiang Chi, Konstantinos N. Plataniotis +1
In this paper, we propose a novel information theoretic surrogate loss; normalized conditional mutual information (NCMI); as a drop in alternative to the de facto cross-entropy (CE…
Coupled Data and Measurement Space Dynamics for Enhanced Diffusion Posterior Sampling
Shayan Mohajer Hamidi, En-Hui Yang, Ben Liang
Inverse problems, where the goal is to recover an unknown signal from noisy or incomplete measurements, are central to applications in medical imaging, remote sensing, and computat…
Towards Undistillable Models by Minimizing Conditional Mutual Information
Linfeng Ye, Shayan Mohajer Hamidi, En-hui Yang
A deep neural network (DNN) is said to be undistillable if, when used as a black-box input-output teacher, it cannot be distilled through knowledge distillation (KD). In this case,…
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…