Papers with Code 아카이브
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/PlanQK/variational-quanvolutional-neural-networks
공식 코드Variational Quanvolutional Neural Networks with enhanced image encoding
https://github.com/Pointcept/Pointcept
공식 코드https://github.com/PolinaKirichenko/flows_ood
공식 코드Why Normalizing Flows Fail to Detect Out-of-Distribution Data
https://github.com/Practical-Formal-Methods/DeepSearch
공식 코드DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks
https://github.com/Pranabiitp/FedDUAL
공식 코드https://github.com/Prasanna1991/LatentMixing
공식 코드Semi-supervised Medical Image Classification with Global Latent Mixing
https://github.com/Prasath2001/Party-Symbol-Classifier
공식 코드https://github.com/ProbabilisticNumerics/cabs
공식 코드https://github.com/PuhongDuan/Fusion-of-Dual-Spatial-Information-for-Hyperspectral-Image-Classification
공식 코드Fusion of Dual Spatial Information for Hyperspectral Image Classification
https://github.com/Puning97/AUTO-for-OOD-detection
공식 코드AUTO: Adaptive Outlier Optimization for Test-Time OOD Detection
https://github.com/Purushothaman-natarajan/Under-water-sonar-image-classification
공식 코드https://github.com/Purushothaman-natarajan/VALE-Explainer
공식 코드https://github.com/QData/AdversarialDNN-Playground
공식 코드Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning
https://github.com/QData/C-Tran
공식 코드https://github.com/QUVA-Lab/e2cnn
공식 코드https://github.com/QingyuanWang/dyce
공식 코드DyCE: Dynamically Configurable Exiting for Deep Learning Compression and Real-time Scaling
https://github.com/QingyuanWang/tinysaver
공식 코드https://github.com/QuIIL/CAMP
공식 코드https://github.com/QuIIL/TQx
공식 코드Towards a text-based quantitative and explainable histopathology image analysis
https://github.com/Quiltomics/NLERL
공식 코드Modeling natural language emergence with integral transform theory and reinforcement learning
https://github.com/RAIVNLab/LLC
공식 코드LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
https://github.com/ROBOTICSENGINEER/Automatic-Open-World-Reliability-Assessment
공식 코드https://github.com/ROBOTICSENGINEER/Open_World_Learning_Without_Labels
공식 코드A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels
https://github.com/RanchoGoose/PViT
공식 코드PViT: Prior-augmented Vision Transformer for Out-of-distribution Detection
https://github.com/Rand2AI/GRNN
공식 코드GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning
https://github.com/Randl/kmeans_selfsuper
공식 코드Self-Supervised Learning for Large-Scale Unsupervised Image Clustering
https://github.com/RaptorMai/online-continual-learning
공식 코드Online Continual Learning in Image Classification: An Empirical Survey
https://github.com/RayRuizhiLiao/joint_chestxray
공식 코드Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
https://github.com/RayRuizhiLiao/mutual_info_img_txt
공식 코드Multimodal Representation Learning via Maximization of Local Mutual Information
https://github.com/Reza-esfandiarpoor/pseudo-shots
공식 코드Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
https://github.com/RickZack/FedSeq
공식 코드Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients
https://github.com/Rishit-dagli/Astroformer
공식 코드Astroformer: More Data Might not be all you need for Classification
https://github.com/RobinBruegger/PartiallyReversibleUnet
공식 코드A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
https://github.com/RobinBruegger/RevTorch
공식 코드A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
https://github.com/RobustBench/robustbench
공식 코드RobustBench: a standardized adversarial robustness benchmark
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.