4 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.CV2026
StableTTA: Improving Vision Model Performance by Training-free Test-Time Adaptation Methods
Zheng Li, Jerry Cheng, Huanying Helen Gu
Ensemble methods improve predictive performance but often incur high memory and computational costs. We identify an aggregation instability induced by nonlinear projection and voti…
cs.LG2023
Rotation Invariant Quantization for Model Compression
Joseph Kampeas, Yury Nahshan, Hanoch Kremer +4
Post-training Neural Network (NN) model compression is an attractive approach for deploying large, memory-consuming models on devices with limited memory resources. In this study,…
cs.CV2021★ 4 cited
FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
Yang Lin, Tianyu Zhang, Peiqin Sun +2
Network quantization significantly reduces model inference complexity and has been widely used in real-world deployments. However, most existing quantization methods have been deve…