https://github.com/roozbeh-yz/similarities
공식 코드Using Wavelets to Analyze Similarities in Image-Classification Datasets
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/royg27/pag-rob
공식 코드Do Perceptually Aligned Gradients Imply Adversarial Robustness?
https://github.com/roymiles/simple-recipe-distillation
공식 코드Understanding the Role of the Projector in Knowledge Distillation
https://github.com/rsCPSyEu/ovd_cod
공식 코드https://github.com/rshaojimmy/ECCV2020-OSAD
공식 코드Open-set Adversarial Defense with Clean-Adversarial Mutual Learning
https://github.com/rsim-tu-berlin/bigearthnet-pipeline
공식 코드reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
https://github.com/rsim-tu-berlin/rico-hdl
공식 코드reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
https://github.com/rstless-research/ncod-learning-with-noisy-labels
공식 코드Learning with Noisy Labels through Learnable Weighting and Centroid Similarity
https://github.com/rstrudel/segmenter
공식 코드https://github.com/rsummers11/CADLab
공식 코드Anatomy-specific classification of medical images using deep convolutional nets
https://github.com/rtaori/data_feedback
공식 코드Data Feedback Loops: Model-driven Amplification of Dataset Biases
https://github.com/rtu715/nas-bench-360
공식 코드NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks
https://github.com/ruckbreasoning/openima
공식 코드https://github.com/rucv/MMCD_DA
공식 코드Multiple Classifiers Based Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/rucv/SGNet
공식 코드SGNet: A Super-class Guided Network for Image Classification and Object Detection
https://github.com/rucv/channeldiversification
공식 코드A Discriminative Channel Diversification Network for Image Classification
https://github.com/ruichen0424/ab-bnn
공식 코드A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network
https://github.com/ruiqirichard/eegeyenet-vit
공식 코드ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Data
https://github.com/ruitian12/resformer
공식 코드https://github.com/ruiyangju/connection_reduction
공식 코드https://github.com/ruiyangju/threshnet
공식 코드ThreshNet: An Efficient DenseNet Using Threshold Mechanism to Reduce Connections
https://github.com/runnanchen/label-free-scene-understanding
공식 코드Towards Label-free Scene Understanding by Vision Foundation Models
https://github.com/rush2406/Self-Supervised-Learning-for-Fine-grained-Image-Classification
공식 코드Self-Supervised Learning for Fine-Grained Image Classification
https://github.com/rushilanirudh/amp
공식 코드https://github.com/russellllaputa/MIRL
공식 코드Masked Image Residual Learning for Scaling Deeper Vision Transformers
https://github.com/russrobin/oscnet
공식 코드OscNet v1.5: Energy Efficient Hopfield Network on CMOS Oscillators for Image Classification
https://github.com/rusty1s/pytorch_geometric
공식 코드SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
https://github.com/rwightman/pytorch-image-models
공식 코드https://github.com/rwl93/bnp4ood
공식 코드A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection
https://github.com/ryan315/single2multi-da
공식 코드SCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial Images
https://github.com/ryansteed/ieat
공식 코드Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases
https://github.com/ryongithub/gatedprompttuning
공식 코드Improving Visual Prompt Tuning for Self-supervised Vision Transformers
https://github.com/ryoo72/una
공식 코드Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection
https://github.com/ryoungj/optdom
공식 코드https://github.com/s-a-malik/multi-few
공식 코드https://github.com/s-mohammad-hashemi/pppl
공식 코드General Domain Adaptation Through Proportional Progressive Pseudo Labeling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.