Image Classification
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/KyleYoung1997/DNNorDermatologist
https://github.com/Kyushik/Predictive-Uncertainty-Estimation-using-Deep-Ensemble
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/LEAP-WS/MDGCN
Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification
https://github.com/LEGO999/BIgBiGAN
The Reversible Residual Network: Backpropagation Without Storing Activations
https://github.com/LEGO999/BigBiGAN-TensorFlow2.0
The Reversible Residual Network: Backpropagation Without Storing Activations
https://github.com/LIU1514/Yolov3-
https://github.com/LJKS/noplants
https://github.com/LJSthu/info-GAN
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/LKLQQ/CBAM
https://github.com/LKLQQ/HireMLP
https://github.com/LKLQQ/Inception-v2
https://github.com/LKLQQ/LEO
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/LKLQQ/ssc_resnet50
https://github.com/LLNL/fastcam
A Benchmark for Interpretability Methods in Deep Neural Networks
https://github.com/LLaVA-VL/LLaVA-NeXT
https://github.com/LMaxence/Cifar10_Classification
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/LTTM/IL-SemSegm
https://github.com/LYWH/oppo_face_vat
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
https://github.com/LachezarPetrov/manifold_mixup_vgg16
Manifold Mixup: Better Representations by Interpolating Hidden States
https://github.com/LamaLenny/Adversarial-Attack
https://github.com/Lamikins/public_exp
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
https://github.com/Lan1991Xu/ONE_NeurIPS2018
https://github.com/LanXiaoPang613/C2MT
https://github.com/LanXiaoPang613/PSSCL
https://github.com/LaurentLava/Lime
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/LawrenceMMStewart/Adversarial_Attack
https://github.com/Ldpe2G/PCANet
PCANet: A Simple Deep Learning Baseline for Image Classification?
https://github.com/LeChangAlex/GAN-Metric
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/LeeDoYup/FixMatch-pytorch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/LeeGitaek/Kaggle_Solving
https://github.com/LeeJunHyun/Image_Segmentation
https://github.com/Legoons/Melanoma_classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Legoons/Whale_Classification
https://github.com/LeiLiu-byte/the-Essence-of-LT-Rocognition
https://github.com/LeilaMahmoodi/Noisy-Labels-Idea
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.