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General Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

11

보존된 평가 기록

19

코드

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

https://github.com/MS-Mind/MS-Code-08/tree/main/MTCNN

Mask Scoring R-CNN

https://github.com/MS-Mind/MS-Code-08/tree/main/meta-baseline

Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning

https://github.com/MS-P3/code7/tree/main/vivit

ViViT: A Video Vision Transformer

https://github.com/MS-P3/code7/tree/main/wav2vec2_with_lm

wav2vec: Unsupervised Pre-training for Speech Recognition

https://github.com/MTG/music-ner

Recognizing Musical Entities in User-generated Content

https://github.com/MY-Swich/SOLO

SOLO: Segmenting Objects by Locations

https://github.com/MY-Swich/SOLO_my

SOLO: Segmenting Objects by Locations

https://github.com/MY-Swich/solo-len

SOLO: Segmenting Objects by Locations

https://github.com/MaDongao/SiftingGAN

SiftingGAN: Generating and Sifting Labeled Samples to Improve the Remote Sensing Image Scene Classification Baseline in vitro

https://github.com/MachineLearningBCAM/MRCpy

Generalized Maximum Entropy for Supervised Classification

https://github.com/MacroMayhem/OnMixup

On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks

https://github.com/MacroMayhem/SharpenFocus-Pytorch

Sharpen Focus: Learning with Attention Separability and Consistency

https://github.com/MaczekO/AttentionNetworkProject

Attention Augmented Convolutional Networks

https://github.com/MagnusCaligo/EvoCNN

Evolving Deep Convolutional Neural Networks for Image Classification

https://github.com/MahmoudWahdan/dialog-nlu

BERT for Joint Intent Classification and Slot Filling

https://github.com/MaiHon/Solo

SOLO: Segmenting Objects by Locations

https://github.com/MalayAgarwal-Lee/MesoNet-DeepFakeDetection

Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

https://github.com/MalayAgarwal-Lee/SteeringWheelAnglePredictor

Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

https://github.com/MaliParag/ScanSSD

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/ManishShettyM/NLP-Offensive-Text-Detection

Detecting Hate Speech in Social Media

https://github.com/ManuelVs/NNForTextClassification

Convolutional Neural Networks for Sentence Classification

https://github.com/ManuelVs/NeuralNetworks

Convolutional Neural Networks for Sentence Classification

https://github.com/MarcAntoineAlex/darts

DARTS: Differentiable Architecture Search

https://github.com/MarcelRobeer/ContrastiveExplanation

Contrastive Explanations with Local Foil Trees

https://github.com/MarcoSaku/Spiking-C3D

Spiking Deep Networks with LIF Neurons

https://github.com/MariefW/6.-trainingYOLO

YOLO9000: Better, Faster, Stronger

https://github.com/MarinePICOT/Adversarial-Robustness-via-Fisher-Rao-Regularization

Self-Adaptive Training: beyond Empirical Risk Minimization

https://github.com/Marius-Juston/AutoCNN

Automatically designing CNN architectures using genetic algorithm for image classification

https://github.com/Mariya1285/Codeathon

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

https://github.com/Markin-Wang/MGANet

Mask Guided Attention For Fine-Grained Patchy Image Classification

https://github.com/Markin-Wang/Mask-Guided-Attention-for-Fine-grained-Patchy-Image-Classification

Mask Guided Attention For Fine-Grained Patchy Image Classification

https://github.com/MarkoLewis-Projects/Sign_language_detection

Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

https://github.com/MartinGer/Attention-Augmented-Convolutional-Networks

Attention Augmented Convolutional Networks

https://github.com/MartinGer/Bottleneck-Transformers-for-Visual-Recognition

Attention Augmented Convolutional Networks

https://github.com/MartinPawel/ProbabilisticallyRobustRecourse

Actionable Recourse in Linear Classification

https://github.com/MartinThoma/HASY

The HASYv2 dataset

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