https://github.com/duggalrahul/REST
REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
Papers with Code 아카이브
Model Compression
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 2
- 보존된 평가 기록
- 14
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/dzqjorking/transpose
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/ecolab-postech/owq
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
https://github.com/eparisotto/ActorMimic
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
https://github.com/eth-easl/deltazip
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
https://github.com/eunanomist/PKD_BERT
https://github.com/for-ai/TD
https://github.com/giakoumoglou/distillers
https://github.com/goodluck4s/bert-
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/google-research/bleurt
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/google-research/google-research
https://github.com/google-research/tapas
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/gpapamak/distilling_model_knowledge
https://github.com/greenbitai/low_bit_llama
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
https://github.com/gsp-27/pytorch_Squeezenet
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://github.com/hahnyuan/llmviewer
https://github.com/halo090770/bert
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/hmkx/hinerv
HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation
https://github.com/huanyuhello/CoDiNet
CoDiNet: Path Distribution Modeling with Consistency and Diversity for Dynamic Routing
https://github.com/huawei-noah/DAFL
https://github.com/huawei-noah/Data-Efficient-Model-Compression
https://github.com/huawei-noah/KD-NLP/tree/main/Annealing_KD
https://github.com/huawei-noah/kd-nlp
https://github.com/huggingface/block_movement_pruning
Training with Quantization Noise for Extreme Model Compression
https://github.com/huggingface/text-generation-inference
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
https://github.com/hustzxd/LSQuantization
https://github.com/hyang1990/energy_constrained_compression
https://github.com/hyang1990/sparsity_quantization_joint
https://github.com/ihaeyong/pfnr
https://github.com/ihaeyong/pnr
https://github.com/intellabs/model-compression-research-package
To prune, or not to prune: exploring the efficacy of pruning for model compression
https://github.com/isadrtdinov/wavenet
Empirical Evaluation of Deep Learning Model Compression Techniques on the WaveNet Vocoder
https://github.com/ito-rafael/once-for-all-2
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/jakc4103/DFQ
https://github.com/jesperhauch/polynomial_deep_learning
https://github.com/jiweibo/imagenet
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.