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코드

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

https://github.com/BFD91/facial-beauty-rating-tinder-bot

SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction

https://github.com/BIG-CHENG/FaceRec

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/BPYap/Question-Generation

EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

https://github.com/BPfuelb/CF_in_DNNs

A comprehensive, application-oriented study of catastrophic forgetting in DNNs

https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/ResMLP

ResMLP: Feedforward networks for image classification with data-efficient training

https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/CrossViT

CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

https://github.com/Backdrop9019/adda_pytorch-pseudo-mixup-

Adversarial Discriminative Domain Adaptation

https://github.com/Backdrop9019/pytorch_adda_mixup

Adversarial Discriminative Domain Adaptation

https://github.com/BaladityaY/lemniscate_reproduce_bdd

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/BaladityaY/lemniscate_vis

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/BalrajAshwath/Adversarial-Domain_Adaptation_Paper_Implementations

Adversarial Discriminative Domain Adaptation

https://github.com/BaptisteH3089/ImbLearning

Cost-Sensitive Support Vector Machines

https://github.com/BarathTamas/JetBot-Thesis

Concrete Autoencoders for Differentiable Feature Selection and Reconstruction

https://github.com/BarbaAlGhul/recommender-system-thesis

In Defense of the Triplet Loss for Person Re-Identification

https://github.com/BeanGreen247/Python-AI-Arts

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/BenjaminLiuPenrose/UnsupervisedImage

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/Benzlxs/benzlxs.github.io

Efficient and accurate object detection with simultaneous classification and tracking

https://github.com/Bersaelor/AAPLMetalImageRecognition

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

https://github.com/Bhushan-Jagtap-2013/Adversarial_Attack_on_RNN

Crafting Adversarial Input Sequences for Recurrent Neural Networks

https://github.com/BilcSergiu/DAGAN

Sampling Generative Networks

https://github.com/BingYu94860/HGNN-Node-Classification

Revisiting Semi-Supervised Learning with Graph Embeddings

https://github.com/BioIntelligence-Lab/openjphpy

ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

https://github.com/Blimeo/rotnet

Unsupervised Representation Learning by Predicting Image Rotations

https://github.com/Blitzkrieg37/Kinesic

Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks

https://github.com/Blue-Giant/MRFGAP_NET

Multi-scale Receptive Fields Graph Attention Network for Point Cloud Classification

https://github.com/BoLiu-SVCL/meta-open

Few-Shot Open-Set Recognition using Meta-Learning

https://github.com/BochaoJ-Lilly/InceptionTime

InceptionTime: Finding AlexNet for Time Series Classification

https://github.com/BonaventureR/person-reid

In Defense of the Triplet Loss for Person Re-Identification

https://github.com/BordiaS/layoutlm

Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification

https://github.com/BorealisAI/towards-better-sel-cls

SelectiveNet: A Deep Neural Network with an Integrated Reject Option

https://github.com/BorisovDm/CPLE_SSL

Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification

https://github.com/BouchardLab/deprecated_process_ecog

Deep learning as a tool for neural data analysis: speech classification and cross-frequency coupling in human sensorimotor cortex

https://github.com/BouchardLab/process_ecog

Deep learning as a tool for neural data analysis: speech classification and cross-frequency coupling in human sensorimotor cortex

https://github.com/Boyiliee/SITT

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

https://github.com/Brain-Inspired-Computing/Final-Project

Spiking Deep Networks with LIF Neurons

https://github.com/Bribak/DeepConnection

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

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