https://github.com/zexuanqiu/hihpq
공식 코드HiHPQ: Hierarchical Hyperbolic Product Quantization for Unsupervised Image Retrieval
Papers with Code 아카이브
Quantization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).
Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zh460045050/vqgan-lc
공식 코드Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%
https://github.com/zhang-guangyi/semantic-mdjcm
공식 코드From Analog to Digital: Multi-Order Digital Joint Coding-Modulation for Semantic Communication
https://github.com/zhang-xd18/qcrnet
공식 코드Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback
https://github.com/zhangsichengsjtu/afpq
공식 코드https://github.com/zhangxinfd/speechtokenizer
공식 코드SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language Models
https://github.com/zhao-yiqun/roomdesigner
공식 코드https://github.com/zhaojw1998/accomontage-3
공식 코드Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling
https://github.com/zhaoweicai/hwgq
공식 코드Deep Learning with Low Precision by Half-wave Gaussian Quantization
https://github.com/zhaoyue-zephyrus/bsq-vit
공식 코드Image and Video Tokenization with Binary Spherical Quantization
https://github.com/zhen-dong/hawq
공식 코드HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision
https://github.com/zhengchen3/ECML_VQRIM
공식 코드Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization
https://github.com/zhengzangw/sequence-scheduling
공식 코드Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline
https://github.com/zhenye234/xcodec
공식 코드Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Model
https://github.com/zhexinli/Q-ViT-DeiT
공식 코드Q-ViT: Fully Differentiable Quantization for Vision Transformer
https://github.com/zhichaoxu-shufe/beyond-perplexity-compression-safety-eval
공식 코드Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression
https://github.com/zhiqic/keyposs
공식 코드KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration
https://github.com/zhitengli/arb-llm
공식 코드ARB-LLM: Alternating Refined Binarizations for Large Language Models
https://github.com/zhitengli/bidrn
공식 코드BinaryHPE: 3D Human Pose and Shape Estimation via Binarization
https://github.com/zhuang-group/mesa
공식 코드https://github.com/zhuang-group/saq
공식 코드https://github.com/zhuhanqing/APOLLO
공식 코드https://github.com/zhuogege1943/ftbnn
공식 코드FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going Beyond
https://github.com/zijianchen98/band-2k
공식 코드BAND-2k: Banding Artifact Noticeable Database for Banding Detection and Quality Assessment
https://github.com/zip-group/mesa
공식 코드https://github.com/zip-group/saq
공식 코드https://github.com/ziplab/mesa
공식 코드https://github.com/ziplab/ptqd
공식 코드PTQD: Accurate Post-Training Quantization for Diffusion Models
https://github.com/ziplab/qllm
공식 코드QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models
https://github.com/ziplab/qtool
공식 코드https://github.com/ziplab/saq
공식 코드https://github.com/ziweiwangthu/seernet
공식 코드Learning Accurate Performance Predictors for Ultrafast Automated Model Compression
https://github.com/zixiangzhou916/ude
공식 코드https://github.com/ziyadsheeba/qfat
공식 코드https://github.com/zjersey/lightseq-arm
공식 코드https://github.com/zjq0455/PTQ-Bench
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.