https://github.com/jannik-brinkmann/multilingual-features
CausalGym: Benchmarking causal interpretability methods on linguistic tasks
Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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Knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jasonakoun/signsgd-fault-tolerance
signSGD with Majority Vote is Communication Efficient And Fault Tolerant
https://github.com/jaychoi12/LG_KD
https://github.com/jaywalnut310/Vector-Quantized-Autoencoders
https://github.com/jbellis/jvector
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
https://github.com/jchen98/compression
https://github.com/jerrytony/mmdetection
https://github.com/jertubiana/ProteinMotifRBM
https://github.com/jessefokkinga/MechanismsOfAction
https://github.com/jeykigung/P5
https://github.com/jfc4050/detect-to-track
https://github.com/jhaprince/multibully
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/jiahuiiiiii/adapi
https://github.com/jiajunhua/facebookresearch-Detectron
https://github.com/jichengYUAN/mmdetectionCust
https://github.com/jielin-qiu/mm_robustness
Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift
https://github.com/jiequancui/LBGAT
https://github.com/jim-schwoebel/allie
https://github.com/jincan333/lot
https://github.com/jinfagang/manadet
https://github.com/jingyunx/Deformation-Flow-Based-Two-stream-Network
https://github.com/jinhaoduan/sar
Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
https://github.com/jinhaoduan/shifting-attention-to-relevance
Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
https://github.com/jireh-father/mmdetection_210507
https://github.com/jixiaozhong/RealSR
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
https://github.com/jk96491/C-COMA
https://github.com/jk96491/SMAC
https://github.com/jkcrosby3/FashionMNST
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/jkooy/Parameter-efficient-Fine-tuning-for-Vision-Transformers
Parameter-efficient Model Adaptation for Vision Transformers
https://github.com/jmhessel/pycocoevalcap
https://github.com/jmorrison57/ContinualLearningProject
https://github.com/jms0923/mmdet_tta
https://github.com/jndeng/DACSDC-DeepZ
https://github.com/joheras/darknet-colab
https://github.com/johnsonkee/graduate_design
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
https://github.com/johntiger1/multimodal_fairness
Multitask learning and benchmarking with clinical time series data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.