https://github.com/bradyfu/awesome-multimodal-large-language-models
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
Papers with Code 아카이브
Knowledge Distillation
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- 하위 태스크
- 2
- 평가표
- 11
- 보존된 평가 기록
- 90
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/bragancas/vnet_pytorch-atriaseg2018
https://github.com/brandontrabucco/cider
https://github.com/bravegroup/sheetcopilot
SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models
https://github.com/brown-palm/ObjectPrompt
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/brucewlee/lftk
Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach
https://github.com/bruthyu/bpt-vlm
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/bspanjers/AIFBAREND
https://github.com/bstadie/third_person_im
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/buptdbj/darknet-windows-linux
https://github.com/buyeah1109/KEN
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/buyeah1109/finc
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/bytedance/next-vit
https://github.com/c3210927/point_cnn
https://github.com/cadia-lvl/fastspeech2
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/caihaocong/Yolov3_AlexeyAB_Version
https://github.com/caihuaiguang/CHG-Shapley-for-Data-Valuation
https://github.com/calderkatyal/CPSC483FinalProject
https://github.com/cameronchoi/r3det-copy
https://github.com/camma-public/mcit-ig
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets
https://github.com/camma-public/rendezvous
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets
https://github.com/camma-public/rendezvous-in-time
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets
https://github.com/carla-recourse/CARLA
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
https://github.com/caskcsg/sentemb
DistilCSE: Effective Knowledge Distillation For Contrastive Sentence Embeddings
https://github.com/castorini/nuggetizer
Ragnarök: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track
https://github.com/castorini/tct_colbert
Distilling Dense Representations for Ranking using Tightly-Coupled Teachers
https://github.com/cataai/posex
State-specific protein-ligand complex structure prediction with a multi-scale deep generative model
https://github.com/cathywu/rllab-multiagent
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/cbfinn/maml_rl
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/cch1999/posecheck
Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?
https://github.com/cdpierse/transformers-interpret
Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach
https://github.com/cesarojuliana/feature_importance_fairness
https://github.com/changminwu/expandergnn
https://github.com/chenfengzhao/gnnhls
https://github.com/chenjiyan2001/paddle-FGCNN
BARS-CTR: Open Benchmarking for Click-Through Rate Prediction
https://github.com/chenkang455/usp-gaussian
SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.