https://github.com/anonymous0519/hsod-bit
공식 코드DMSSN: Distilled Mixed Spectral-Spatial Network for Hyperspectral Salient Object Detection
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/ansebi/wain22_sim_env
공식 코드Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasets
https://github.com/ansfl/MEMS-IMU-Denoising
공식 코드https://github.com/ant-research/easytemporalpointprocess
공식 코드https://github.com/antoalli/3d_os
공식 코드https://github.com/anton-bushuiev/ppiref
공식 코드https://github.com/antoniocarta/data-agnostic-consolidation
공식 코드Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning
https://github.com/anugyas/ood-on-nae
공식 코드https://github.com/anupamaray/ensembleqml_application
공식 코드https://github.com/anwar-said/neurograph
공식 코드NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
https://github.com/anwarmaxsum/agla
공식 코드https://github.com/aobol/kamnet
공식 코드KamNet: An Integrated Spatiotemporal Deep Neural Network for Rare Event Search in KamLAND-Zen
https://github.com/aorogat/CBench
공식 코드CBench: Towards Better Evaluation of Question Answering Over Knowledge Graphs
https://github.com/aparna-aketi/cross_feature_contrastive_loss
공식 코드Cross-feature Contrastive Loss for Decentralized Deep Learning on Heterogeneous Data
https://github.com/apcl-research/jam-cgpt
공식 코드https://github.com/aperezlebel/benchmark_mv_approaches
공식 코드Benchmarking missing-values approaches for predictive models on health databases
https://github.com/apliko-xyz/pytdc
공식 코드https://github.com/appfl/appfl
공식 코드Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
https://github.com/apple/ml-cvnets
공식 코드RangeAugment: Efficient Online Augmentation with Range Learning
https://github.com/apple/ml-dr
공식 코드Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement
https://github.com/apple/ml-recurrent-drafter
공식 코드Recurrent Drafter for Fast Speculative Decoding in Large Language Models
https://github.com/apple/ml-synthbias
공식 코드Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs
https://github.com/apple2373/wbc-benchmark
공식 코드Benchmarking White Blood Cell Classification Under Domain Shift
https://github.com/applied-ai-research-lab/orchestrator-agent-trust
공식 코드https://github.com/applied-machine-learning-lab/erase
공식 코드ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems
https://github.com/aprbw/ood_electricity_forecasting_during_covid-19
공식 코드https://github.com/aprilsveryown/semantically-guided-continual-learning
공식 코드Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning
https://github.com/apurba-nsu-rnd-lab/lila-boti
공식 코드https://github.com/arcaninez/crld
공식 코드Cross-View Consistency Regularisation for Knowledge Distillation
https://github.com/arcaninez/odm3d
공식 코드ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object Detection
https://github.com/archettialberto/federated_survival_datasets
공식 코드Heterogeneous Datasets for Federated Survival Analysis Simulation
https://github.com/archiel19/frax4
공식 코드https://github.com/archiplab-linfengzhang/contrastive-deep-supervision
공식 코드https://github.com/architsharma97/earl_benchmark
공식 코드Autonomous Reinforcement Learning: Formalism and Benchmarking
https://github.com/archmaester/proto2proto
공식 코드https://github.com/arena-rosnav/arena-tools
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.