https://github.com/TUM-AIMED/2.5DAttention
공식 코드Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
Papers with Code 아카이브
Image Classification
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- 평가표
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- 보존된 평가 기록
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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/Tab-ct/STSC-SNN
공식 코드https://github.com/TaeseongYoon/DAEDL
공식 코드Uncertainty Estimation by Density Aware Evidential Deep Learning
https://github.com/TailinZhou/FedFA
공식 코드https://github.com/Talented-Q/MSMAE
공식 코드https://github.com/Tangshitao/Semi-supervised-Adaptive-Distillation
공식 코드Learning Efficient Detector with Semi-supervised Adaptive Distillation
https://github.com/TashinAhmed/CloudsClassification
공식 코드Classification and understanding of cloud structures via satellite images with EfficientUNet
https://github.com/TeaPearce/Bayesian_NN_Ensembles
공식 코드Uncertainty in Neural Networks: Approximately Bayesian Ensembling
https://github.com/Tencent/tencent-ml-images
공식 코드Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning
https://github.com/ThomasNorr/QPM
공식 코드QPM: Discrete Optimization for Globally Interpretable Image Classification
https://github.com/TiagoFilipeSousaGoncalves/survey-attention-medical-imaging
공식 코드A survey on attention mechanisms for medical applications: are we moving towards better algorithms?
https://github.com/TianshuXie/Cut-Thumbnail
공식 코드Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural Network
https://github.com/Tiiiger/SGC
공식 코드https://github.com/TimDettmers/sparse_learning
공식 코드Sparse Networks from Scratch: Faster Training without Losing Performance
https://github.com/TimRoith/BregmanLearning
공식 코드https://github.com/TimSalimans/weight_norm
공식 코드Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
https://github.com/TonyLianLong/UnsupervisedSelectiveLabeling
공식 코드Unsupervised Selective Labeling for More Effective Semi-Supervised Learning
https://github.com/Top-Ranger/text_adversarial_attack
공식 코드https://github.com/Tpap/TNet
공식 코드https://github.com/Tsingularity/FRN
공식 코드Few-Shot Classification with Feature Map Reconstruction Networks
https://github.com/UBCDingXin/CellCount_TinyBBBC005
공식 코드https://github.com/UBCDingXin/cGAN-based_KD
공식 코드https://github.com/UCSC-REAL/CAL
공식 코드A Second-Order Approach to Learning with Instance-Dependent Label Noise
https://github.com/UCSC-REAL/HOC
공식 코드Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
https://github.com/UCSC-REAL/cores
공식 코드Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
https://github.com/UKPLab/emnlp2018-activation-functions
공식 코드Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks
https://github.com/UKPLab/tacl2020-interactive-ranking
공식 코드Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation
https://github.com/UMBCvision/CompReSS
공식 코드CompRess: Self-Supervised Learning by Compressing Representations
https://github.com/UMBCvision/Hidden-Trigger-Backdoor-Attacks
공식 코드https://github.com/UmarKhalidcs/RODD
공식 코드RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection
https://github.com/UnchartedRLab/SSP
공식 코드Self-Supervised Pretraining for Differentially Private Learning
https://github.com/VADERASU/visual-analytics-for-deep-transfer-learning
공식 코드A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes
https://github.com/VDIGPKU/ContinualContrastiveLearning
공식 코드https://github.com/VICO-UoE/DatasetCondensation
공식 코드Dataset Condensation with Differentiable Siamese Augmentation
https://github.com/VICO-UoE/L2I
공식 코드Learning to Impute: A General Framework for Semi-supervised Learning
https://github.com/VICO-UoE/URL
공식 코드Universal Representation Learning from Multiple Domains for Few-shot Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.