https://github.com/DannyMerkx/speech2image
Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech
Papers with Code 아카이브
Quantization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
- 32
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/DeadAt0m/ActiveSparseShifts-PyTorch
https://github.com/DeadAt0m/LSQ-PyTorch
https://github.com/DeadAt0m/LSQFakeQuantize-PyTorch
https://github.com/DominikFHG/TensorQuant
TensorQuant - A Simulation Toolbox for Deep Neural Network Quantization
https://github.com/EEESlab/CMix-NN
https://github.com/EdoardoBotta/RQ-VAE-Recommender
https://github.com/Enderdead/Pytorch_Quantize_impls
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
https://github.com/Entropy-xcy/bitnet158
BitNet: Scaling 1-bit Transformers for Large Language Models
https://github.com/Eunhui-Kim/SSPQ
https://github.com/FengJiaChunFromSYSU/fastText
https://github.com/FudanMPL/SecMML
https://github.com/Galitski-theory-group/quantum-nn
https://github.com/Gedeon-m-gedus/compressed_object_detection
https://github.com/GeorgeLuImmortal/Hierarchical-BERT-Model-with-Limited-Labelled-Data
https://github.com/GitHubSprint/fasttext4j
https://github.com/HLasse/wav2vec_finetune
Unsupervised Cross-lingual Representation Learning for Speech Recognition
https://github.com/HarunoriKawano/Wav2vec2.0
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
https://github.com/IST-DASLab/smart-quantizer
https://github.com/IntelLabs/distiller
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
https://github.com/Irtza/Keras_model_compression
https://github.com/JDAI-CV/dabnn
Forward and Backward Information Retention for Accurate Binary Neural Networks
https://github.com/JGU-VC/progressive_stochastic_binarization
https://github.com/Janus-Shiau/awd-lstm-tensorflow
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
https://github.com/JiahaoYan98/Pruning-ADMM
https://github.com/JoegameZhou/MindSpeech
Unsupervised Cross-lingual Representation Learning for Speech Recognition
https://github.com/Jopyth/BMXNet
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
https://github.com/JoungheeKim/Non-Attentive-Tacotron
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
https://github.com/KaiyuanZh/censor
https://github.com/KarenUllrich/Tutorial_BayesianCompressionForDL
https://github.com/KazuhisaFujita/asc-using-som
https://github.com/Kelvinyu1117/LSQ-implementation
https://github.com/Kinetikm/fastTextRelearning
https://github.com/Kinetikm/fasttextRelearnExperiment
https://github.com/KneeHaw/StoX-Net
StoX-Net: Stochastic Processing of Partial Sums for Efficient In-Memory Computing DNN Accelerators
https://github.com/KwangHoonAn/PACT
PACT: Parameterized Clipping Activation for Quantized Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.