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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/Dipeshtamboli/Image-Classification-and-Localization-using-Multiple-Instance-Learning
공식 코드Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning
https://github.com/Dishant-P/Similarity-based-clustering-Official-research-module
공식 코드Similarity-Based Clustering for Enhancing Image Classification Architectures
https://github.com/Div290/I-SplitEE
공식 코드I-SplitEE: Image classification in Split Computing DNNs with Early Exits
https://github.com/DmitriiShubin/Variance-Aware-Training
공식 코드https://github.com/DoctorKey/R2D2.pytorch
공식 코드Repetitive Reprediction Deep Decipher for Semi-Supervised Learning
https://github.com/DominikFilipiak/KGTN-ens
공식 코드KGTN-ens: Few-Shot Image Classification with Knowledge Graph Ensembles
https://github.com/DreadPiratePsyopus/Fine_Grained_Clf
공식 코드https://github.com/EIDOSlab/unveiling-covid19-from-cxr
공식 코드Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
https://github.com/ENSTA-U2IS-AI/torch-uncertainty
공식 코드https://github.com/EPFL-VILAB/MultiMAE
공식 코드https://github.com/ESA-PhiLab/QNN4EO
공식 코드Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing
https://github.com/EddCBen/Classifying-Textual-Data-with-pretrained-Vision-Models-through-Transfer-Learning-and-Data-Transforms
공식 코드https://github.com/Edge-AI-Acceleration-Lab/RBFleX-NAS
공식 코드https://github.com/Element-Research/dpnn
공식 코드SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://github.com/ElementAI/bilevel_augment
공식 코드Learning Data Augmentation with Online Bilevel Optimization for Image Classification
https://github.com/ElementAI/embedding-propagation
공식 코드Embedding Propagation: Smoother Manifold for Few-Shot Classification
https://github.com/Eli-YiLi/ScaleNet
공식 코드Data-Driven Neuron Allocation for Scale Aggregation Networks
https://github.com/EliSchwartz/DeltaEncoder
공식 코드Delta-encoder: an effective sample synthesis method for few-shot object recognition
https://github.com/ElisaGiesecke/augmented-RK-Nets
공식 코드Classification with Runge-Kutta networks and feature space augmentation
https://github.com/EmilienDupont/augmented-neural-odes
공식 코드https://github.com/EmoryMLIP/DOvsOD_NeuralODEs
공식 코드https://github.com/EmoryMLIP/HybridRFM
공식 코드Avoiding The Double Descent Phenomenon of Random Feature Models Using Hybrid Regularization
https://github.com/Engler93/Self-Supervised-Autogenous-Learning
공식 코드Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks
https://github.com/Eric-mingjie/network-slimming
공식 코드Learning Efficient Convolutional Networks through Network Slimming
https://github.com/EscVM/Efficient-CapsNet
공식 코드Efficient-CapsNet: Capsule Network with Self-Attention Routing
https://github.com/Expedit-LargeScale-Vision-Transformer/Expedit-DINO
공식 코드Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning
https://github.com/Expedit-LargeScale-Vision-Transformer/Expedit-DPT
공식 코드Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning
https://github.com/Expedit-LargeScale-Vision-Transformer/Expedit-Segmenter
공식 코드Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning
https://github.com/Externalhappy/DPA
공식 코드DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language Models
https://github.com/FBLADL/ACPL
공식 코드ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
https://github.com/FannierPeng/SgVA-CLIP
공식 코드https://github.com/FatemehAskari/MSENet
공식 코드https://github.com/Fchaubard/gradient_agreement_filtering
공식 코드https://github.com/Fei-Long121/DeepBDC
공식 코드Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification
https://github.com/Felipe-Gomez/gdp-numeric
공식 코드$(\varepsilon, δ)$ Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees
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