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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/PB17151764/2020UM-Summer-Research

Deep Retinal Image Understanding

https://github.com/PCJohn/cata-forget

Overcoming catastrophic forgetting in neural networks

https://github.com/PINTO0309/Keras-OneClassAnomalyDetection

Learning Deep Features for One-Class Classification

https://github.com/PJamesY/category_classification-NLP-

Convolutional Neural Networks for Sentence Classification

https://github.com/POSTECH-CVLab/FastPointTransformer

Point Transformer

https://github.com/POSTECH-CVLab/point-transformer

Point Transformer

https://github.com/PPPrior/i3d-pytorch

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

https://github.com/PRBonn/bonnet

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/PRIS-CV/PCA-Net

Progressive Co-Attention Network for Fine-grained Visual Classification

https://github.com/PRIS-CV/Top-Down-Spatial-Attention-Loss

Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained Features

https://github.com/PRIS-CV/knowledge-transfer-based-FGVC

Knowledge Transfer Based Fine-grained Visual Classification

https://github.com/PV-Lab/autoXRD

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

https://github.com/PabloVD/21cmDeepLearning

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

https://github.com/Pacmed/ehr_ood_detection

Uncertainty estimation for classification and risk prediction on medical tabular data

https://github.com/Pacmed/uncertainty-classification-tabular-data

Uncertainty estimation for classification and risk prediction on medical tabular data

https://github.com/PaddlePaddle/PASSL

All Tokens Matter: Token Labeling for Training Better Vision Transformers

https://github.com/PaddlePaddle/PaddleClas

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/PaddlePaddle/PaddleDetection

YOLO9000: Better, Faster, Stronger

https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/few_shot/pet

Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference

https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/sentiment_analysis/textcnn

Convolutional Neural Networks for Sentence Classification

https://github.com/PaddlePaddle/PaddleOCR

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/maml

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

https://github.com/PaddlePaddle/PaddleRec/tree/release/2.1.0/models/contentunderstanding/textcnn

Convolutional Neural Networks for Sentence Classification

https://github.com/PaddlePaddle/PaddleSeg

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/PaddlePaddle/PaddleSpeech

Domain-Adversarial Training of Neural Networks

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/model_zoo/recognition/videoswin.md

Video Swin Transformer

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/slowfast.md

SlowFast Networks for Video Recognition

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/timesformer.md

Is Space-Time Attention All You Need for Video Understanding?

https://github.com/PaddlePaddle/models

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/Pandade1997/tera_asvproof

Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders

https://github.com/Pangoraw/GeoSSL

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/PaperCodeReview/BarlowTwins-TF

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/Paperspace/hyperopt-keras-sample

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

https://github.com/PasqualeAuriemma/GCN-DIFFPOOL

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/Pathakvishnu/Microsoft-Malware-Detection--Kaggle

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

https://github.com/PatrickPrakash/LiptoSpeech

LipNet: End-to-End Sentence-level Lipreading

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