5 papers
RFPrompt: Prompt-Based Expert Adaptation of the Large Wireless Model for Modulation Classification
Md Raihan Uddin, Tolunay Seyfi, Fatemeh Afghah
Automatic modulation classification (AMC) in real-world deployments demands robustness to distribution shifts arising from hardware impairments, unseen propagation environments, an…
ZO-SAM: Zero-Order Sharpness-Aware Minimization for Efficient Sparse Training
Jie Ji, Gen Li, Kaiyuan Deng +2
Deep learning models, despite their impressive achievements, suffer from high computational costs and memory requirements, limiting their usability in resource-constrained environm…
Modality-Aware SAM: Sharpness-Aware-Minimization Driven Gradient Modulation for Harmonized Multimodal Learning
Hossein R. Nowdeh, Jie Ji, Xiaolong Ma +1
In multimodal learning, dominant modalities often overshadow others, limiting generalization. We propose Modality-Aware Sharpness-Aware Minimization (M-SAM), a model-agnostic frame…
Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-Design
Gen Li, Zhihao Shu, Jie Ji +4
Deep neural networks (DNNs) are frequently employed in a variety of computer vision applications. Nowadays, an emerging trend in the current video distribution system is to take ad…
Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting
Gen Li, Jie Ji, Minghai Qin +5
As deep convolutional neural networks (DNNs) are widely used in various fields of computer vision, leveraging the overfitting ability of the DNN to achieve video resolution upscali…