Qat3 [Paper Review] Quantized Feature Distillation for Network Quantization 논문 원본 : https://arxiv.org/abs/2307.10638 Quantized Feature Distillation for Network QuantizationNeural network quantization aims to accelerate and trim full-precision neural network models by using low bit approximations. Methods adopting the quantization aware training (QAT) paradigm have recently seen a rapid growth, but are often conceptually comparxiv.org Abstract & Introduction신경망 양자화(Neura.. 2024. 12. 29. [Paper Review] Self-Supervised Quantization-Aware Knowledge Distillation 논문 원본 : https://arxiv.org/abs/2403.11106 Self-Supervised Quantization-Aware Knowledge DistillationQuantization-aware training (QAT) and Knowledge Distillation (KD) are combined to achieve competitive performance in creating low-bit deep learning models. However, existing works applying KD to QAT require tedious hyper-parameter tuning to balance the weiarxiv.org AbstractQuantization-aware trainin.. 2024. 12. 1. [Paper Review] OverComing Oscillations in Quantization-Aware Training 논문 원본 : https://arxiv.org/abs/2203.11086 Overcoming Oscillations in Quantization-Aware TrainingWhen training neural networks with simulated quantization, we observe that quantized weights can, rather unexpectedly, oscillate between two grid-points. The importance of this effect and its impact on quantization-aware training (QAT) are not well-understarxiv.org Abstract딥러닝 모델을 simulated quantizati.. 2024. 11. 28. 이전 1 다음
