https://github.com/mahmad00/Artifacts-of-DR-on-Hybrid-CNN-for-HSIC/blob/main/Artifacts_of_DR_on_Hybrid_CNN_for_HSIC.ipynb
공식 코드Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN
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/mahmad00/HSI-Traditional-to-Deep-Models
공식 코드Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects
https://github.com/mahmad00/MHSSMamba
공식 코드Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification
https://github.com/mahmad00/MorpMamba
공식 코드Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification
https://github.com/mahmad00/Multiclass-AL-HSIC-using-ELM-SVM-KNN-LORSAL
공식 코드https://github.com/mahmad00/conventional-to-transformer-for-hyperspectral-image-classification-survey-2024
공식 코드https://github.com/mahmad00/disjoint-sampling-for-hyperspectral-image-classification
공식 코드https://github.com/mahmad00/pyformer
공식 코드Pyramid Hierarchical Transformer for Hyperspectral Image Classification
https://github.com/mahmad000/atl-sst
공식 코드Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image Classification
https://github.com/mahmad000/diffformer
공식 코드DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification
https://github.com/mahmad000/morpmamba
공식 코드Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification
https://github.com/mahmoudsajjadi/dtfl
공식 코드Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approach
https://github.com/mahuanaaa/intta
공식 코드Spurious Feature Eraser: Stabilizing Test-Time Adaptation for Vision-Language Foundation Model
https://github.com/mail-research/randomized_defenses
공식 코드Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks
https://github.com/makamoa/hyplex
공식 코드Real-time Hyperspectral Imaging in Hardware via Trained Metasurface Encoders
https://github.com/mala-lab/adaptod
공식 코드Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation
https://github.com/mala-lab/negprompt
공식 코드Learning Transferable Negative Prompts for Out-of-Distribution Detection
https://github.com/mala-lab/opencil
공식 코드OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental Learning
https://github.com/manjaryp/ImageForgeryFairness
공식 코드Towards Exploring Fairness in Visual Transformer based Natural and GAN Image Detection Systems
https://github.com/manuelknott/dino-vit_fruit_quality_assessment
공식 코드Facilitated machine learning for image-based fruit quality assessment
https://github.com/mao-ku/ems
공식 코드EMS: Efficient and Effective Massively Multilingual Sentence Embedding Learning
https://github.com/maohaos2/PosthocUQ
공식 코드https://github.com/mapillary/inplace_abn
공식 코드In-Place Activated BatchNorm for Memory-Optimized Training of DNNs
https://github.com/maple-research-lab/CLSA
공식 코드https://github.com/maple-research-lab/EnAET
공식 코드https://github.com/maple-research-lab/caco
공식 코드https://github.com/mapleleaf6/zode
공식 코드Boosting Out-of-Distribution Detection with Multiple Pre-trained Models
https://github.com/mapolinario94/CODE-CL
공식 코드CODE-CL: Conceptor-Based Gradient Projection for Deep Continual Learning
https://github.com/mapolinario94/LLS-DNN
공식 코드LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural Activity Synchronization
https://github.com/mapolinario94/s-tllr
공식 코드S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks
https://github.com/marclafon/gallop
공식 코드GalLoP: Learning Global and Local Prompts for Vision-Language Models
https://github.com/marclafon/heatood
공식 코드Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection
https://github.com/marcosvendramini/GeMOS
공식 코드https://github.com/marcotcr/lime-experiments
공식 코드"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/marcusklasson/GroceryStoreDataset
공식 코드A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels
https://github.com/marialymperaiou/classification-in-the-wild
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.