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/koala719/bvit
공식 코드https://github.com/kobybibas/pnml_ood_detection
공식 코드Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection
https://github.com/koichikuriyama/latentaugment
공식 코드LatentAugment: Dynamically Optimized Latent Probabilities of Data Augmentation
https://github.com/kojima-takeshi188/cfa
공식 코드https://github.com/konstantinkobs/SimLoss
공식 코드https://github.com/korawat-tanwisuth/pouf
공식 코드POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained models
https://github.com/koriavinash1/BioExp
공식 코드Abstracting Deep Neural Networks into Concept Graphs for Concept Level Interpretability
https://github.com/koriavinash1/visualdebates
공식 코드https://github.com/kostadinovshalon/oln-ssos
공식 코드Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier Synthesis
https://github.com/kostas1515/aglu
공식 코드https://github.com/kowshikthopalli/decider
공식 코드DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation
https://github.com/kqp1227/sensitive-channel-pruning
공식 코드Achieving Fairness Through Channel Pruning for Dermatological Disease Diagnosis
https://github.com/kreimanlab/occlusion-classification
공식 코드https://github.com/krishnatarun/ssl_dgc
공식 코드https://github.com/krisnapinasthika/sparseswin
공식 코드https://github.com/kschwethelm/hyperboliccv
공식 코드Fully Hyperbolic Convolutional Neural Networks for Computer Vision
https://github.com/ksreenivasan/OOD_Federated_Learning
공식 코드Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
https://github.com/ktran1/SSDL
공식 코드Semi-supervised dictionary learning with graph regularization and active points
https://github.com/ku-cvlab/maskingdepth
공식 코드MaskingDepth: Masked Consistency Regularization for Semi-supervised Monocular Depth Estimation
https://github.com/kuai-lab/sound-guided-semantic-image-manipulation
공식 코드https://github.com/kuanghuei/clean-net
공식 코드CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
https://github.com/kuluhan/pre-dfkd
공식 코드Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay
https://github.com/kumar-shridhar/ProbAct-Probabilistic-Activation-Function
공식 코드ProbAct: A Probabilistic Activation Function for Deep Neural Networks
https://github.com/kumar-shridhar/PyTorch-BayesianCNN
공식 코드A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
https://github.com/kunalbanerjee/softmax_alternatives
공식 코드https://github.com/kundajelab/abstention
공식 코드Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation
https://github.com/kundajelab/labelshiftexperiments
공식 코드Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation
https://github.com/kunzhan/infomatch
공식 코드InfoMatch: Entropy Neural Estimation for Semi-Supervised Image Classification
https://github.com/kunzhan/semihsi
공식 코드Graph-Weighted Contrastive Learning for Semi-Supervised Hyperspectral Image Classification
https://github.com/kurtisdavid/ImageAnchors
공식 코드GANchors: Realistic Image Perturbation Distributions for Anchors Using Generative Models
https://github.com/kvignesh1420/gnn_collapse
공식 코드A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
https://github.com/kwonjunn01/hi-mapper
공식 코드Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping
https://github.com/kyk120/fpet
공식 코드Faster Parameter-Efficient Tuning with Token Redundancy Reduction
https://github.com/kyle-one/Context-Encoding-of-Detected-Buildings
공식 코드https://github.com/kyle-one/signa
공식 코드Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.