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cs.CV2026
Denoised Variance-Based Pruning with Optimal Brain Bias Compensation
Geon Tack Lee, Jaegul Choo, Kang Eun Jeon
Vision Transformers (ViTs) achieve state-of-the-art performance but carry massive computational overhead that restricts edge deployment. Although structural pruning has emerged as…
cs.CV2026
TASQ: Temporal-Adaptive Bit Sparsification Quantization for Diffusion Models
Seokho Han, Dongwei Wang, Jinhee Kim +4
Static quantization assigns one weight precision to every denoising step. To preserve quality, that precision must accommodate the most quantization-sensitive step, even though man…
cs.CV2025
Efficient Multi-bit Quantization Network Training via Weight Bias Correction and Bit-wise Coreset Sampling
Jinhee Kim, Jae Jun An, Kang Eun Jeon +1
Multi-bit quantization networks enable flexible deployment of deep neural networks by supporting multiple precision levels within a single model. However, existing approaches suffe…