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General Classification

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평가표

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보존된 평가 기록

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/donghyeonk/fastText1607

Bag of Tricks for Efficient Text Classification

https://github.com/dongjun-Lee/text-classification-models-tf

Convolutional Neural Networks for Sentence Classification

https://github.com/doragd/text-classification-pytorch

A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

https://github.com/dougbrion/pytorch-classification-uncertainty

Evidential Deep Learning to Quantify Classification Uncertainty

https://github.com/douglaswinstonr/GAEE

Comparison of VCA and GAEE algorithms for Endmember Extraction

https://github.com/dpaddon/product_image_categorisation

A Unified Model with Structured Output for Fashion Images Classification

https://github.com/dpalominop/ULMFit

Universal Language Model Fine-tuning for Text Classification

https://github.com/dqxiu/kassess

SOLO: Segmenting Objects by Locations

https://github.com/dragen1860/DARTS-PyTorch

DARTS: Differentiable Architecture Search

https://github.com/dragen1860/MAML-Pytorch

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/dragen1860/MAML-Pytorch-RL

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/dragen1860/Reptile-Pytorch

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/dragon20002/STLC_Server

YOLO9000: Better, Faster, Stronger

https://github.com/dreasysnail/deconv_paragraph_represention

Deconvolutional Paragraph Representation Learning

https://github.com/dreasysnail/textGAN_public

Deconvolutional Paragraph Representation Learning

https://github.com/dropoutlabs/encrypted-skin-cancer-detection

Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)

https://github.com/drscotthawley/SPNet

YOLO9000: Better, Faster, Stronger

https://github.com/drv-agwl/ViViT-pytorch

ViViT: A Video Vision Transformer

https://github.com/drwuHUST/TLBCI

Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline

https://github.com/dsikar/efficient-compact-fire-detection-cnn

Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection

https://github.com/dsindex/iclassifier

BERT for Joint Intent Classification and Slot Filling

https://github.com/dtak/lit

Learning Qualitatively Diverse and Interpretable Rules for Classification

https://github.com/dtanoglidis/DeepShadows

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/dtriepke/Graph_Convolutional_Network

Graph Convolutional Networks for Text Classification

https://github.com/dtthi/darknetAlexeyAB

YOLO9000: Better, Faster, Stronger

https://github.com/ducanhnguyen/batchnormalization

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

https://github.com/duong18/BiasedWalk

BiasedWalk: Biased Sampling for Representation Learning on Graphs

https://github.com/duongttr/SWP

A Large-Scale Car Dataset for Fine-Grained Categorization and Verification

https://github.com/dv-fenix/Domain-Adaptation

Domain-Adversarial Training of Neural Networks

https://github.com/dvbckle/Process-Fault-Identification-with-CNN

Dataset: Rare Event Classification in Multivariate Time Series

https://github.com/dvndra/car_detection_yolo

YOLO9000: Better, Faster, Stronger

https://github.com/dvornikita/fewshot_ensemble

Diversity with Cooperation: Ensemble Methods for Few-Shot Classification

https://github.com/dwaithe/darknet3AB

YOLO9000: Better, Faster, Stronger

https://github.com/dwaithe/yolov2

YOLO9000: Better, Faster, Stronger

https://github.com/dwiggles/AIX360-withdata

Leveraging Latent Features for Local Explanations

https://github.com/dyadav4/Autonomous-driving-application-car-detection

YOLO9000: Better, Faster, Stronger

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