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평가표

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보존된 평가 기록

19

코드

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

https://github.com/eriklindernoren/Keras-GAN

Adversarial Autoencoders

https://github.com/eriklindernoren/PyTorch-GAN

Adversarial Autoencoders

https://github.com/erlendd/ddan

Domain-Adversarial Training of Neural Networks

https://github.com/ermongroup/graphite

Graphite: Iterative Generative Modeling of Graphs

https://github.com/ermuur/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/ernestkck/RML2018

Over the Air Deep Learning Based Radio Signal Classification

https://github.com/esadsetts/Mod_4_project

A Structured Self-attentive Sentence Embedding

https://github.com/esantamariavazquez/EEG-Inception

EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification

https://github.com/eske/multivec

BilBOWA: Fast Bilingual Distributed Representations without Word Alignments

https://github.com/ethz-asl/background_foreground_segmentation

Overcoming catastrophic forgetting in neural networks

https://github.com/europa1610/Tensorflow-Shufflenet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/evantkchong/LEPAR

Cost-Sensitive Label Embedding for Multi-Label Classification

https://github.com/evarsha/YOLO-Object-Detection

YOLO9000: Better, Faster, Stronger

https://github.com/ex4sperans/freesound-classification

Improving Pairwise Ranking for Multi-label Image Classification

https://github.com/examsherpa/Melanoma-Image-Classification

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/explosion/floret

Bag of Tricks for Efficient Text Classification

https://github.com/eyalbetzalel/residual-flows

Invertible Residual Networks

https://github.com/ezelikman/Context-Is-Everything

Contextual Salience for Fast and Accurate Sentence Vectors

https://github.com/fabhertz95/HATS

HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification

https://github.com/fabian57fabian/prototypical-networks-few-shot-learning

Prototypical Networks for Few-shot Learning

https://github.com/fabioasdias/ubao

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/fabyangliu/AGCNN-Sentence-Classification

An Attention-Gated Convolutional Neural Network for Sentence Classification

https://github.com/fabyangliu/AGCNN_sentence_classification

An Attention-Gated Convolutional Neural Network for Sentence Classification

https://github.com/facebook/fb-caffe-exts

Going Deeper with Convolutions

https://github.com/facebook/fb.resnet.torch

Some Improvements on Deep Convolutional Neural Network Based Image Classification

https://github.com/facebookarchive/fb.resnet.torch

Some Improvements on Deep Convolutional Neural Network Based Image Classification

https://github.com/facebookresearch/ClassyVision

Exploring the Limits of Weakly Supervised Pretraining

https://github.com/facebookresearch/DomainBed

Domain-Adversarial Training of Neural Networks

https://github.com/facebookresearch/InferSent

SentEval: An Evaluation Toolkit for Universal Sentence Representations

https://github.com/facebookresearch/ModelRatatouille

Recognition in Terra Incognita

https://github.com/facebookresearch/R2Plus1D

Video Classification with Channel-Separated Convolutional Networks

https://github.com/facebookresearch/WSL-Images

Exploring the Limits of Weakly Supervised Pretraining

https://github.com/facebookresearch/clip-rocket

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/facebookresearch/detectron

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/facebookresearch/jacobian_regularizer

Robust Learning with Jacobian Regularization

https://github.com/facebookresearch/pycls

Aggregated Residual Transformations for Deep Neural Networks

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