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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/boyiliee/sitta

The Plant Pathology 2020 challenge dataset to classify foliar disease of apples

https://github.com/bplank/teaching-dl4nlp

Convolutional Neural Networks for Sentence Classification

https://github.com/bpuladi/Automated-PD-L1-Scoring

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/brando90/Generalization-Puzzles-in-Deep-Networks

A Surprising Linear Relationship Predicts Test Performance in Deep Networks

https://github.com/brando90/overparam_experiments

A Surprising Linear Relationship Predicts Test Performance in Deep Networks

https://github.com/brandonrobertz/AcademicUrlTitles

Semi-Supervised Learning with Ladder Networks

https://github.com/brandonserna/sentiment2emoji

EmoTxt: A Toolkit for Emotion Recognition from Text

https://github.com/bratao/PySeqLab

ADADELTA: An Adaptive Learning Rate Method

https://github.com/brent-murray/tr3d_pointaugdgcnn

PointAugment: an Auto-Augmentation Framework for Point Cloud Classification

https://github.com/brian-moser/dartsrenet

DARTS: Differentiable Architecture Search

https://github.com/brianyan918/CNN-for-Sentence-Classification

Convolutional Neural Networks for Sentence Classification

https://github.com/brightmart/ai_law

Convolutional Neural Networks for Sentence Classification

https://github.com/brightmart/roberta_zh

Pre-Training with Whole Word Masking for Chinese BERT

https://github.com/brightmart/text_classification

A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

https://github.com/brunocampos01/tcc-ufsc-grad

Financial Time Series Data Processing for Machine Learning

https://github.com/brycexu/MR-Residual-Net

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

https://github.com/bspanjers/AIFBAREND

Mitigating Unwanted Biases with Adversarial Learning

https://github.com/buaaYYC/faster-rcnn-tensorflow

An Implementation of Faster RCNN with Study for Region Sampling

https://github.com/buaayangchun/malwrebackdoor

Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers

https://github.com/buiduchanh/Learning-Deep-Features-for-One-Class-Classification

Learning Deep Features for One-Class Classification

https://github.com/bung87/fastText

Bag of Tricks for Efficient Text Classification

https://github.com/buptdbj/darknet-windows-linux

YOLO9000: Better, Faster, Stronger

https://github.com/burakalperen/Pytorch-Semantic-Segmentation

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

https://github.com/burklight/MINE-PyTorch

MINE: Mutual Information Neural Estimation

https://github.com/butkej/mil4cyto

Deep Instance-Level Hard Negative Mining Model for Histopathology Images

https://github.com/butsugiri/shape

Simple Recurrent Units for Highly Parallelizable Recurrence

https://github.com/bw-tech/Language-Representation-Models-for-Fine-Grained-Sentiment-Classification

Language Representation Models for Fine-Grained Sentiment Classification

https://github.com/bw2color/bw2color

XGBoost: A Scalable Tree Boosting System

https://github.com/bzhangGo/lrn

Simple Recurrent Units for Highly Parallelizable Recurrence

https://github.com/bzhangGo/zero

Fixup Initialization: Residual Learning Without Normalization

https://github.com/bzz/LangID

Bag of Tricks for Efficient Text Classification

https://github.com/c1ph3rr/Very-Deep-Convolutional-Networks-For-Large-Scale-Image-Recognition

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/caesarea38/doclangid

A Closer Look at Few-shot Classification

https://github.com/caggursoy/Cross-Age-Effect

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

https://github.com/caggursoy/crossageeffect

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

https://github.com/cahya-wirawan/indonesian-language-models

Universal Language Model Fine-tuning for Text Classification

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