https://github.com/yeyimilk/llm-vision-marine-animals
공식 코드Benchmarking Large Language Models for Image Classification of Marine Mammals
Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/yeyimilk/llmgeo
공식 코드LLMGeo: Benchmarking Large Language Models on Image Geolocation In-the-wild
https://github.com/yflyl613/tiny-newsrec
공식 코드Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation
https://github.com/ygjwd12345/TransDA
공식 코드https://github.com/ygjwd12345/UCD
공식 코드Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation
https://github.com/yhyang-myron/sampart3d-website
공식 코드https://github.com/yhydhx/chatgpt-api
공식 코드https://github.com/yhzhu99/covid-ehr-benchmarks
공식 코드https://github.com/yhzhu99/ehr-llm-benchmark
공식 코드https://github.com/yhzhu99/medagentboard
공식 코드https://github.com/yhzhu99/pyehr
공식 코드https://github.com/yhzhu99/pyehr-playground
공식 코드https://github.com/yichaolu/traffic4cast2021
공식 코드Learning to Transfer for Traffic Forecasting via Multi-task Learning
https://github.com/yichaolu/traffic4cast2022
공식 코드An Efficient Two-stage Gradient Boosting Framework for Short-term Traffic State Estimation
https://github.com/yichizhang98/sam4mis
공식 코드Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions
https://github.com/yihong-97/oass
공식 코드https://github.com/yikaiw/RS-Nets
공식 코드Resolution Switchable Networks for Runtime Efficient Image Recognition
https://github.com/yikunhan42/tinyllm
공식 코드https://github.com/yimeng0701/CKD_pytorch
공식 코드Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers
https://github.com/yinghao-li/muben
공식 코드MUBen: Benchmarking the Uncertainty of Molecular Representation Models
https://github.com/yinhefeng/ANCR
공식 코드Affine Non-negative Collaborative Representation Based Pattern Classification
https://github.com/yinwenpeng/BenchmarkingZeroShot
공식 코드Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach
https://github.com/yixinghuang/chatgpt-benchmark-on-radiation-oncology
공식 코드https://github.com/yixuantt/MultiHop-RAG
공식 코드MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries
https://github.com/yiyudl/delicate
공식 코드Chemical transformer compression for accelerating both training and inference of molecular modeling
https://github.com/yizhen20133868/ci-tod
공식 코드https://github.com/yizhidamiaomiao/DoppelgangerGraph
공식 코드https://github.com/yjiangcm/lion
공식 코드Lion: Adversarial Distillation of Proprietary Large Language Models
https://github.com/yjoh12/cxr-segmentation-by-adain-based-domain-adaptation-and-knowledge-distillation
공식 코드CXR Segmentation by AdaIN-based Domain Adaptation and Knowledge Distillation
https://github.com/ykang/gratis
공식 코드GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
https://github.com/ykang/tsgeneration
공식 코드GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
https://github.com/ykrmm/benchmarktw
공식 코드Temporal receptive field in dynamic graph learning: A comprehensive analysis
https://github.com/ylongqi/FoodDist
공식 코드Yum-me: A Personalized Nutrient-based Meal Recommender System
https://github.com/ymoslem/model-compression
공식 코드Efficient Speech Translation through Model Compression and Knowledge Distillation
https://github.com/yoichii/nashpobench2api
공식 코드https://github.com/yongchanghao/multi-task-nat
공식 코드Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.