Papers with Code 아카이브
General Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Algorithms trying to solve the general task of classification.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kazuto1011/grad-cam-pytorch
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/kbpi314/iclust_paper
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kbsriharsha/FacialEmotionAnalysis
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/kc-ml2/darts
https://github.com/kebasaa/SCIO-read
https://github.com/keerthi165/depthEstimation
Deep attention-based classification network for robust depth prediction
https://github.com/keetsky/Net_ghostVLAD-pytorch
https://github.com/keil555/AAE_paddle
https://github.com/keillernogueira/FDSI
https://github.com/keithRebello/ULMFiT_sentiment_analysis
Universal Language Model Fine-tuning for Text Classification
https://github.com/keithyuck/Object-Tracking
Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition
https://github.com/kemaloksuz/aLRPLoss-AblationExperiments
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection
https://github.com/ken-power/CVND-FacialKeypointDetection
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
https://github.com/kenkenling/NLP
https://github.com/kentsommer/keras-inceptionV4
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/kenya-sk/mc_dropout_tensorflow
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/keras-team/keras
https://github.com/keras-team/keras-io/blob/master/examples/vision/ipynb/gradient_centralization.ipynb
Gradient Centralization: A New Optimization Technique for Deep Neural Networks
https://github.com/keras-team/keras-io/blob/master/examples/vision/vivit.py
https://github.com/keshav47/Face-Recognition-And-Verification
https://github.com/keunwoochoi/MSD_split_for_tagging
Convolutional Recurrent Neural Networks for Music Classification
https://github.com/keunwoochoi/music-auto_tagging-keras
Convolutional Recurrent Neural Networks for Music Classification
https://github.com/kevin851066/Deep_Learning_for_Computer_Vision
https://github.com/kevindeangeli/capsuleNetwork
Investigating Capsule Networks with Dynamic Routing for Text Classification
https://github.com/kevinghst/ICT
Interpolation Consistency Training for Semi-Supervised Learning
https://github.com/kevinmusgrave/pytorch-adapt
https://github.com/kevinwong2013/COMS4995_Team_4_Zero_Shot_Classifier
Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
https://github.com/kh-kim/simple-nmt
https://github.com/kh-kim/simple-ntc
https://github.com/khaled2ahmed/k2a
https://github.com/khanrc/pt.darts
https://github.com/khawajatalhahaseeb/Class-Activation-Map-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/khazit/Lip2Word
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/khazit/LipNet
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/khrlimam/facenet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.