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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/D-X-Y/GDAS

DARTS: Differentiable Architecture Search

https://github.com/D-X-Y/NAS-Projects

DARTS: Differentiable Architecture Search

https://github.com/D-X-Y/ResNeXt-DenseNet

Aggregated Residual Transformations for Deep Neural Networks

https://github.com/DCYN/Ramdomized-Clinical-Trail-Classification

PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts

https://github.com/DCalhas/siamese_schizophrenia_eeg

On the use of Pairwise Distance Learning for Brain Signal Classification with Limited Observations

https://github.com/DIDSR/DeepLearningCamelyon_II

Deep Learning for Identifying Metastatic Breast Cancer

https://github.com/DInesh1234-wq/tyredataset

YOLO9000: Better, Faster, Stronger

https://github.com/DMarwan/NutricIA

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/DW-yejing/fasttext4j-jdk6

Bag of Tricks for Efficient Text Classification

https://github.com/DaCreasy/DLFinal

Transfer learning for music classification and regression tasks

https://github.com/DaikiTanaka-UT/JointOptimization

Joint Optimization Framework for Learning with Noisy Labels

https://github.com/DamianValle/ScarceClassifier

Unsupervised Representation Learning by Predicting Image Rotations

https://github.com/Daniel-Liu-c0deb0t/3D-Neural-Network-Adversarial-Attacks

Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers

https://github.com/DanielShalam/SOT

Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions

https://github.com/DarkGeekMS/Semantic_Segmentation_Models_Keras

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

https://github.com/Darth-Kronos/Unsupervised-Domain-Adaptation

Domain-Adversarial Training of Neural Networks

https://github.com/Data-drone/attention_augmented_cnn

Attention Augmented Convolutional Networks

https://github.com/DataXujing/PNASNet_pytorch

Progressive Neural Architecture Search

https://github.com/DataZwer/BaseTensorFlowModel

Convolutional Neural Networks for Sentence Classification

https://github.com/DataZwer/CNNTextClassification

Convolutional Neural Networks for Sentence Classification

https://github.com/DaveFClarke/ml_bias_checking

Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

https://github.com/DavianYang/yolo.ai

YOLO9000: Better, Faster, Stronger

https://github.com/David200308/Sequential-Convolutional-Recurrent-Neural-Networks-for-Fast-Automatic-Modulation-Classification

Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

https://github.com/DavidMortensen/Bachelor-readinglist

Relation Classification via Recurrent Neural Network

https://github.com/DeLightCMU/MAL

Multiple Anchor Learning for Visual Object Detection

https://github.com/DeLightCMU/MAL-inference

Multiple Anchor Learning for Visual Object Detection

https://github.com/DeadAt0m/ActiveSparseShifts-PyTorch

All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification

https://github.com/DeadNud1e/Local_Concepts_Accumulation

Bag of Tricks for Retail Product Image Classification

https://github.com/Deci-AI/super-gradients

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Deeksha-5/C-Net_Pytorch_Implementation

C-Net: A Reliable Convolutional Neural Network for Biomedical Image Classification

https://github.com/DeepGraphLearning/GMNN

Revisiting Semi-Supervised Learning with Graph Embeddings

https://github.com/Deepak3994/Grad-Cam

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

https://github.com/Deepak3994/grad-cam-editted

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

https://github.com/DeepakRaya/Cognitive_classification_RNN_EEG_images

Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

https://github.com/Deepayan137/Adapting-OCR

Universal Language Model Fine-tuning for Text Classification

https://github.com/Deeplodocus/COCO-with-YOLO

YOLO9000: Better, Faster, Stronger

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