https://github.com/maincold2/FFNeRV
공식 코드FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videos
Papers with Code 아카이브
Model Compression
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Model Compression is an actively pursued area of research over the last few years with the goal of deploying state-of-the-art deep networks in low-power and resource limited devices without significant drop in accuracy. Parameter pruning, low-rank factorization and weight quantization are some of the proposed methods to compress the size of deep networks.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mandt-lab/bayesian-ac
공식 코드https://github.com/mandt-lab/vbq
공식 코드https://github.com/marsjacobs/kd-qat-large-enc
공식 코드https://github.com/maxc01/addtree
공식 코드https://github.com/mbarsbey/implicit-compressibility
공식 코드Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD
https://github.com/mbarsbey/sgd_comp_gen
공식 코드Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks
https://github.com/microsoft/DeepSpeed
공식 코드DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
https://github.com/microsoft/NeuralSpeech
공식 코드LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search
https://github.com/microsoft/fnl_paper
공식 코드Initialization and Regularization of Factorized Neural Layers
https://github.com/microsoft/semiparametric-distillation
공식 코드https://github.com/ming-k9/mft
공식 코드https://github.com/mingsun-tse/regularization-pruning
공식 코드https://github.com/mit-han-lab/amc
공식 코드AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/mit-han-lab/neurips-micronet
공식 코드https://github.com/mitscha/strassennets
공식 코드https://github.com/mklimasz/transferable-modularity
공식 코드Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation
https://github.com/model-compression/lossless_compression
공식 코드"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach
https://github.com/mohaimenz/acdnet
공식 코드https://github.com/mohaimenz/pruning-xnor
공식 코드Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devices
https://github.com/mohammad-mozaffari/slim
공식 코드SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
https://github.com/motefly/LMA
공식 코드https://github.com/musab-r/TCN
공식 코드Teacher-Class Network: A Neural Network Compression Mechanism
https://github.com/mvp18/KD-SVD
공식 코드https://github.com/nanguoyu/model-folding-universal
공식 코드Forget the Data and Fine-Tuning! Just Fold the Network to Compress
https://github.com/negedng/ma-fl-mia
공식 코드https://github.com/nervjack2/speech-ssl-compression
공식 코드Is Smaller Always Faster? Tradeoffs in Compressing Self-Supervised Speech Transformers
https://github.com/niutrans/niutensor
공식 코드https://github.com/nizhenliang/Dual-Relation-Knowledge-Distillation
공식 코드https://github.com/noakliu/efficient-foundation-models-survey
공식 코드Designing Large Foundation Models for Efficient Training and Inference: A Survey
https://github.com/nverma1/merging-ffs-compression
공식 코드https://github.com/nvlabs/maskllm
공식 코드MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models
https://github.com/nyunai/faster-llm-survey
공식 코드Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward
https://github.com/ofsoundof/learning_filter_basis
공식 코드Learning Filter Basis for Convolutional Neural Network Compression
https://github.com/ooibc88/modelslicing
공식 코드Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource Constraints
https://github.com/opengvlab/omniquant
공식 코드OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.