Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 11
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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/ningdez/Tianchi_Cancer_303
https://github.com/nithinksath96/MMdetection_TensorRT_FP16
https://github.com/nithinksath96/mmdetection
https://github.com/nitin35/masktrackrcnn
https://github.com/noahj08/DeepConnotation
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark
https://github.com/nocotan/born_again_neuralnet
https://github.com/nopperl/clip_arxiv_pmc
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ntucllab/climage_dataset
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
https://github.com/ntucllab/complementary_cifar
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
https://github.com/nutonomy/nuscenes-devkit
Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking
https://github.com/nvlabs/diff-rt
Sionna: An Open-Source Library for Next-Generation Physical Layer Research
https://github.com/nxphi47/multiagent_crosstranslate
Data Diversification: A Simple Strategy For Neural Machine Translation
https://github.com/oaqa/FlexNeuART
https://github.com/oaqa/knn4qa
https://github.com/omron-sinicx/planning-datasets
OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets
https://github.com/oorrppp2/darknet-pose
https://github.com/open-compass/vlmevalkit
https://github.com/open-dataflow/rare
Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts
https://github.com/openai/cleverhans
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
https://github.com/openai/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/openbiolink/promptsource
Multitask Prompted Training Enables Zero-Shot Task Generalization
https://github.com/openbmb/toolbench
StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models
https://github.com/opendenoising/opendenoising-benchmark
OpenDenoising: an Extensible Benchmark for Building Comparative Studies of Image Denoisers
https://github.com/opendrivelab/uniad
NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking
https://github.com/openml-labs/gama
https://github.com/openml/openml-aslib
https://github.com/opensum/bigae
DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining
https://github.com/openvinotoolkit/anomalib/tree/development/anomalib/models/reverse_distillation
Anomaly Detection via Reverse Distillation from One-Class Embedding
https://github.com/openvinotoolkit/deep-object-reid
Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch
https://github.com/orionbench/orionbench
ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
https://github.com/osilab-kaist/smac_exp
https://github.com/osilab-kaist/smac_plus
https://github.com/osu-nlp-group/hipporag
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
https://github.com/oxwhirl/facmac
https://github.com/oxwhirl/smacv2
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.