논문 리뷰44 [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] Learned Step Size Quantization 논문 원본 : https://arxiv.org/abs/1902.08153 Learned Step Size QuantizationDeep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, but need to overcome the challenge of maintaining high accuracy as precision decreases. Here, we present a method for trainarxiv.org Abstract딥러닝 네트워크는 inference 시 계산 비용을 줄이고 효율성을 높이기 위해 저정밀도(ex... 2024. 11. 30. [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. [X:AI] Flamingo 논문 리뷰 Flamingo: a Visual Language Model for Few-Shot Learning 🦩논문 원본 : https://arxiv.org/abs/2204.14198 Flamingo: a Visual Language Model for Few-Shot LearningBuilding models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this abili.. 2024. 8. 26. [X:AI] DALL-E 논문 리뷰 Zero-Shot Text-to-Image Generation논문 원본 : https://arxiv.org/abs/2102.12092 Zero-Shot Text-to-Image GenerationText-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentatiarxiv.org 2. Method Stage 1Di.. 2024. 8. 20. [X:AI] BLIP 논문 리뷰 Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation논문 원본 : https://arxiv.org/abs/2201.12086 BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and GenerationVision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in eit.. 2024. 8. 20. 이전 1 2 3 4 5 6 ··· 8 다음
