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cs.CV2024
Toward INT4 Fixed-Point Training via Exploring Quantization Error for Gradients
Dohyung Kim, Junghyup Lee, Jeimin Jeon +2
Network quantization generally converts full-precision weights and/or activations into low-bit fixed-point values in order to accelerate an inference process. Recent approaches to…
cs.CV2024
Scheduling Weight Transitions for Quantization-Aware Training
Junghyup Lee, Jeimin Jeon, Dohyung Kim +1
Quantization-aware training (QAT) simulates a quantization process during training to lower bit-precision of weights/activations. It learns quantized weights indirectly by updating…
cs.CV2024
Instance-Aware Group Quantization for Vision Transformers
Jaehyeon Moon, Dohyung Kim, Junyong Cheon +1
Post-training quantization (PTQ) is an efficient model compression technique that quantizes a pretrained full-precision model using only a small calibration set of unlabeled sample…