https://github.com/YuanEZhou/Grounded-Image-Captioning
공식 코드More Grounded Image Captioning by Distilling Image-Text Matching Model
Papers with Code 아카이브
Knowledge Distillation
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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/ZamDimon/liveness-detection
공식 코드https://github.com/Zengyi-Qin/Weakly-Supervised-3D-Object-Detection
공식 코드https://github.com/ZhangYuanhan-AI/OmniBenchmark
공식 코드Benchmarking Omni-Vision Representation through the Lens of Visual Realms
https://github.com/ZhaoJ9014/High_Performance_Face_Recognition
공식 코드https://github.com/Zhaolu-K/HSSBench
공식 코드HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
https://github.com/ZhaomingKong/Denoising-Comparison
공식 코드https://github.com/Zhengbo-Zhang/ITKD
공식 코드https://github.com/Zheyu-Rain/APT-HLS-AI
공식 코드https://github.com/ZhgLiu/GDD
공식 코드https://github.com/Zzzzz1/CSKD
공식 코드Cumulative Spatial Knowledge Distillation for Vision Transformers
https://github.com/a-rebmann/llms4pm
공식 코드Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks
https://github.com/a11to1n3/LimeSoDa
공식 코드https://github.com/a773938364/divkd
공식 코드A Diversity-Enhanced Knowledge Distillation Model for Practical Math Word Problem Solving
https://github.com/aaai-24/generative-based-kd
공식 코드Generative Model-based Feature Knowledge Distillation for Action Recognition
https://github.com/aaandy-zhu/vgcure
공식 코드https://github.com/aabarr/Synthetic-Neurosurgical-Data
공식 코드Zero-shot generation of synthetic neurosurgical data with large language models
https://github.com/aadharna/watts
공식 코드https://github.com/aalto-speech/dbca
공식 코드https://github.com/aalto-speech/mtkd4ser
공식 코드Multi-Teacher Language-Aware Knowledge Distillation for Multilingual Speech Emotion Recognition
https://github.com/aannonymouuss/plms-in-practical-kbqa
공식 코드An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering
https://github.com/aashish-khub/NearestNeighbors
공식 코드N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
https://github.com/aashish2000/anna
공식 코드ANNA: Abstractive Text-to-Image Synthesis with Filtered News Captions
https://github.com/abacusai/xai-bench
공식 코드Synthetic Benchmarks for Scientific Research in Explainable Machine Learning
https://github.com/abdullahsohaill/cs6303-researchproject
공식 코드Deciphering the Underserved: Benchmarking LLM OCR for Low-Resource Scripts
https://github.com/abhi1nandy2/yesbut_dataset
공식 코드https://github.com/abhipec/HAnDS
공식 코드Fine-grained Entity Recognition with Reduced False Negatives and Large Type Coverage
https://github.com/abhrac/xmodal-vit
공식 코드Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval
https://github.com/abin333/logonet
공식 코드LogoNet: a fine-grained network for instance-level logo sketch retrieval
https://github.com/aboveparadise/llmcbench
공식 코드LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment
https://github.com/abr/power_benchmarks
공식 코드Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware
https://github.com/abrain-one/nn-dataset
공식 코드https://github.com/abrain-one/nn-plots
공식 코드https://github.com/abrain-one/nn-vr
공식 코드https://github.com/acadlab/spicepilot
공식 코드SPICEPilot: Navigating SPICE Code Generation and Simulation with AI Guidance
https://github.com/acellera/acegen-open
공식 코드ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.