Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 11
- 보존된 평가 기록
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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/ctuning/ck-mlperf
https://github.com/cuber2460/bert
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/cuguilke/microexpnet
MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images
https://github.com/cui0523/Code6/tree/main/ternarybert
https://github.com/cvlab-yonsei/dkd
Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation
https://github.com/cxa-unique/Simplified-TinyBERT
Simplified TinyBERT: Knowledge Distillation for Document Retrieval
https://github.com/cydiachen/offlinemmdet
https://github.com/dacon-ai/K-fashion-baseline
https://github.com/daerduoCarey/partnet_anno_system
https://github.com/daerduoCarey/partnet_dataset
https://github.com/daiweiworking/BenchmarkDeepLearning
Benchmarking Contemporary Deep Learning Hardware and Frameworks:A Survey of Qualitative Metrics
https://github.com/danijar/crafter
https://github.com/danini/magsac
Image Matching across Wide Baselines: From Paper to Practice
https://github.com/dannima/pae_probe_experiments
https://github.com/daqingliu/coco-caption
https://github.com/dar-tau/lra-benchmark
https://github.com/darshans0200/YOLOTest
https://github.com/darwen9/ckmimagenet
Generative CKM Construction using Partially Observed Data with Diffusion Model
https://github.com/data-iitd/tigger
https://github.com/datalab-georgetown/fairness-and-missing-values
https://github.com/datastax/jvector
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
https://github.com/datcancode/sentence-transformers
Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation
https://github.com/dathudeptrai/TensorflowTTS
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/daveboat/pytorch_focal_loss
https://github.com/davidhalladay/MMDetection
https://github.com/davidpengucf/RAIN
RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation
https://github.com/dc17540/darknet
https://github.com/debeshjha/transnetr
https://github.com/debjani-bhowmick/harmonic-net-pytorch
https://github.com/decisionintelligence/catch
TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
https://github.com/deep-privacy/Voice-Privacy-Challenge-2020
https://github.com/deeplearning-wisc/hypo
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/deepmind/optax
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
https://github.com/deepmodeling/Uni-Mol
Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+
https://github.com/delmalih/MIAS-mammography-obj-detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.