5 accepted papers
Real-World Super-Resolution (RWSR) aims to reconstruct high-resolution images from low-resolution inputs captured under complex, real-life conditions, where diverse distortions result in significant degradation heterogeneity. Many methods rely on degradation representations, yet they struggle with t…
Quantization-aware Training (QAT) enables deep models to adapt to precision loss by simulating quantization. However, existing methods often converge to sub-optimal solutions due to inadequate exploration of quantization solution space. To address this, we propose a novel QAT method, Allowing Oscill…
As large language models (LLMs) are increasingly deployed on resource-constrained edge devices, quantization techniques have been widely adopted to reduce model size and computational requirements. However, this process can expose models to new vulnerabilities. In this work, we introduce the Quantiz…
Gradient sparsification and quantization offer a promising prospect to alleviate the communication overhead problem in distributed learning. However direct combination of the two results in suboptimal solutions due to the fact that sparsification and quantization haven't been learned together. In th…
Model quantization is widely used to compress and accelerate deep neural networks. However recent studies have revealed the feasibility of weaponizing model quantization via implanting quantization-conditioned backdoors (QCBs). These special backdoors stay dormant on released full-precision models b…