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General Classification

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코드

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

https://github.com/Tixierae/deep_learning_NLP

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

https://github.com/Tnorm/SVM_cookietest

Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks

https://github.com/TobiasLee/Text-Classification

Convolutional Neural Networks for Sentence Classification

https://github.com/TonyYaoMSU/TRADES

Theoretically Principled Trade-off between Robustness and Accuracy

https://github.com/TooTouch/WhiteBox-Part1

Visualizing and Understanding Convolutional Networks

https://github.com/Tools4Project/4501Project

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Torilen/TER-LCF-ATEPC-FR

A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction

https://github.com/TotalVariation/Flattenet

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/TqDavid/td

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

https://github.com/Trent-tangtao/embedding

Learning to Make Predictions on Graphs with Autoencoders

https://github.com/TrevorIkky/RegNet

RegNet: Self-Regulated Network for Image Classification

https://github.com/TruongThaoNguyen/kakurenbo

An Empirical Study of Example Forgetting during Deep Neural Network Learning

https://github.com/TrustworthyAI-kr/LFI-CAM

LFI-CAM: Learning Feature Importance for Better Visual Explanation

https://github.com/TsingZ0/PFL-Non-IID

Convolutional Neural Networks for Sentence Classification

https://github.com/TuSimple/resnet.mxnet

Aggregated Residual Transformations for Deep Neural Networks

https://github.com/TwistedW/LGAN_pytorch

Global versus Localized Generative Adversarial Nets

https://github.com/UBCDingXin/cGAN-KD

Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regression

https://github.com/UF-HH/bbbbAnalysis

Reweighting with Boosted Decision Trees

https://github.com/UK-Digital-Heart-Project/lvae_mlp

Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling

https://github.com/UKPLab/coling18-multimodalSurvey

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/UKPLab/linspector-web

What you can cram into a single vector: Probing sentence embeddings for linguistic properties

https://github.com/UNHSAILLab/Sentimental-LIAR-Extended-Corpus-and-Deep-Learning-Models-for-Fake-Claim-Classification

Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification

https://github.com/UTMIST/oneshot-data

The Omniglot challenge: a 3-year progress report

https://github.com/UTMIST/oneshot-rs

The Omniglot challenge: a 3-year progress report

https://github.com/Uchman21/RAW

Recurrent Attention Walk for Semi-supervised Classification

https://github.com/Ugenteraan/Deep_Hierarchical_Classification

Deep Hierarchical Classification for Category Prediction in E-commerce System

https://github.com/UlysseCoteAllard/MyoArmbandDataset

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

https://github.com/UnofficialJuliaMirror/AugmentedGaussianProcesses.jl-38eea1fd-7d7d-5162-9d08-f89d0f2e271e

Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation

https://github.com/UnofficialJuliaMirrorSnapshots/AugmentedGaussianProcesses.jl-38eea1fd-7d7d-5162-9d08-f89d0f2e271e

Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation

https://github.com/Uranium-Deng/Steganalysis-StegoRemoval

CBAM: Convolutional Block Attention Module

https://github.com/UrosOgrizovic/SimpleGoogleQuickdraw

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

https://github.com/VDelv/EEGLearn-Pytorch

Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

https://github.com/VICO-UoE/KD4MTL

Multi-Task Learning as Multi-Objective Optimization

https://github.com/VLL-HD/IB-INN

Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification

https://github.com/VLL-HD/exact_information_bottleneck

Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification

https://github.com/Vakihito/SentimentYoutube

Very Deep Convolutional Networks for Large-Scale Image Recognition

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