Papers with Code 아카이브
Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 33
- 평가표
- 177
- 보존된 평가 기록
- 2,906
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/mariodoebler/test-time-adaptation
공식 코드https://github.com/marksgraham/ddpm-ood
공식 코드Unsupervised 3D out-of-distribution detection with latent diffusion models
https://github.com/marksgraham/transformer-ood
공식 코드Transformer-based out-of-distribution detection for clinically safe segmentation
https://github.com/marrlab/ae-cfe
공식 코드Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification
https://github.com/martin-marek/dirclip
공식 코드https://github.com/martin-ochoa/foobar
공식 코드FooBaR: Fault Fooling Backdoor Attack on Neural Network Training
https://github.com/martinagvilas/vit-cls_emb
공식 코드Analyzing Vision Transformers for Image Classification in Class Embedding Space
https://github.com/martiniani-lab/dynamic-divisive-norm
공식 코드Unconditional stability of a recurrent neural circuit implementing divisive normalization
https://github.com/marziehoghbaie/pipvit
공식 코드PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysis
https://github.com/masih4/Skin-lesion-segmentation-effects-of-the-classification-perfromnce
공식 코드The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification
https://github.com/matanat/GRACE
공식 코드Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
https://github.com/matanat/dae_counterfactual
공식 코드https://github.com/matankleiner/coherence-awareness-in-diffractive-neural-networks
공식 코드https://github.com/matej-ulicny/harmonic-networks
공식 코드https://github.com/matejgrcic/Vistas-NP
공식 코드Dense open-set recognition with synthetic outliers generated by Real NVP
https://github.com/mateuszgabor/rsdtr_compression
공식 코드Reduced storage direct tensor ring decomposition for convolutional neural networks compression
https://github.com/matheusbarrosp/chessmix
공식 코드ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentation
https://github.com/matt-lourens/dynamic-qcnn
공식 코드Hierarchical quantum circuit representations for neural architecture search
https://github.com/matt-lourens/hierarqcal
공식 코드Hierarchical quantum circuit representations for neural architecture search
https://github.com/matthewwicker/StatisticalGuarenteesForBNNs
공식 코드Statistical Guarantees for the Robustness of Bayesian Neural Networks
https://github.com/maurodlt/tensair
공식 코드TensAIR: Real-Time Training of Neural Networks from Data-streams
https://github.com/maxium0526/category-wise-fine-tuning
공식 코드https://github.com/maxium0526/ptrn
공식 코드https://github.com/maxnygma/icla
공식 코드Identity Curvature Laplace Approximation for Improved Out-of-Distribution Detection
https://github.com/mayanbiao1234/geometric-metrics-for-perceptual-manifolds
공식 코드Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds
https://github.com/mayanbiao1234/geometric-metrics-for-perceptual-manifolds-in-deep-neural-networks
공식 코드Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds
https://github.com/mayug/0-shot-llm-vision
공식 코드Do Vision and Language Encoders Represent the World Similarly?
https://github.com/mayug/basetransformers
공식 코드BaseTransformers: Attention over base data-points for One Shot Learning
https://github.com/mayurmallya/deepguide
공식 코드https://github.com/maziarraissi/HPM
공식 코드Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
https://github.com/mboudiaf/TIM
공식 코드https://github.com/mc-e/transcl
공식 코드TransCL: Transformer Makes Strong and Flexible Compressive Learning
https://github.com/mcg-nju/ssd-lt
공식 코드Self Supervision to Distillation for Long-Tailed Visual Recognition
https://github.com/mchancan/deepseqslam
공식 코드DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition
https://github.com/mcmahon-lab/Single-Photon-Detection-Neural-Networks
공식 코드Quantum-limited stochastic optical neural networks operating at a few quanta per activation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.