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Knowledge Distillation

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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://gitlab.com/pierregtch/motor_embedding_benchmark

공식 코드

Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of Datasets

https://gitlab.com/qumphy/d1-code

공식 코드

Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches

https://gitlab.com/vasishtduddu/FaultTolerantNN

공식 코드

Towards Enhancing Fault Tolerance in Neural Networks

https://gitlab.idiap.ch/bob/bob.paper.ijcb2021_synthetic_dataset

공식 코드

On the use of automatically generated synthetic image datasets for benchmarking face recognition

https://gitlab.idiap.ch/bob/bob.paper.ijcb2023_synthdistill

공식 코드

SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data

https://gitlab.lrz.de/hctl/Iris-Obfuscation-Benchmark

공식 코드

Trade-offs in Privacy-Preserving Eye Tracking through Iris Obfuscation: A Benchmarking Study

https://huggingface.co/bdpc/src

공식 코드

Beyond Document Page Classification: Design, Datasets, and Challenges

https://zenodo.org/record/8077639

공식 코드

Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test

https://github.com/0jason000/wukong

Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark

https://github.com/13274086/Tianchi2020

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/13952522076/mmdet0

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/13952522076/mmdet1

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/2023-MindSpore-1/ms-code-1

BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search

https://github.com/2023-MindSpore-1/ms-code-132

CRNN: A Joint Neural Network for Redundancy Detection

https://github.com/2023-MindSpore-1/ms-code-16/tree/main/AlphaPose

AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time

https://github.com/2023-MindSpore-1/ms-code-16/tree/main/adelaide_ea

Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells

https://github.com/2023-MindSpore-1/ms-code-166

TinyBERT: Distilling BERT for Natural Language Understanding

https://github.com/2023-MindSpore-1/ms-code-18/tree/main/lite-hrnet

HRNET: AI on Edge for mask detection and social distancing

https://github.com/2023-MindSpore-1/ms-code-210/tree/main/cnn_direction_model

Structured Sparse R-CNN for Direct Scene Graph Generation

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet101

Focal Loss for Dense Object Detection

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet152

Focal Loss for Dense Object Detection

https://github.com/2023-MindSpore-1/ms-code-220/tree/main/tinybert

Simplified TinyBERT: Knowledge Distillation for Document Retrieval

https://github.com/2023-MindSpore-1/ms-code-223/tree/main/RetinaNet

Focal Loss for Dense Object Detection

https://github.com/2023-MindSpore-1/ms-code-224/tree/main/adelaide_ea

Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells

https://github.com/2023-MindSpore-1/ms-code-44

Focal Loss for Dense Object Detection

https://github.com/2023-MindSpore-1/ms-code-6/tree/main/cnn_direction_model

Structured Sparse R-CNN for Direct Scene Graph Generation

https://github.com/2023-MindSpore-4/Code10/tree/main/Focus-DETR

Knowledge Distillation for Detection Transformer with Consistent Distillation Points Sampling

https://github.com/2023-MindSpore-4/Code10/tree/main/RetinaNet

Focal Loss for Dense Object Detection

https://github.com/2023-MindSpore-4/Code11/tree/main/retinanet_resnet101

Focal Loss for Dense Object Detection

https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/clip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/2023-MindSpore-4/Code4/tree/main/ProC-KD-main

Prototype-guided Cross-task Knowledge Distillation for Large-scale Models

https://github.com/2023-MindSpore-4/Code8/tree/main/cct

Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bge_m3

BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/clip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/distilbert

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/fastspeech2_conformer

FastSpeech 2: Fast and High-Quality End-to-End Text to Speech

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.