https://github.com/jiawangbai/HAT
공식 코드Improving Vision Transformers by Revisiting High-frequency Components
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/jiawei-ren/BalancedMetaSoftmax
공식 코드https://github.com/jiaweizzhao/zero-initialization
공식 코드ZerO Initialization: Initializing Neural Networks with only Zeros and Ones
https://github.com/jiaxuan-li/pnd
공식 코드Partition-and-Debias: Agnostic Biases Mitigation via A Mixture of Biases-Specific Experts
https://github.com/jiequancui/Parametric-Contrastive-Learning
공식 코드https://github.com/jiequancui/ResLT
공식 코드https://github.com/jieshibo/tocom
공식 코드Token Compensator: Altering Inference Cost of Vision Transformer without Re-Tuning
https://github.com/jiexuanyan/cprfl
공식 코드Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification
https://github.com/jiforcen/ordered-weighted-pooling
공식 코드https://github.com/jihyechoi77/concepts-for-ood
공식 코드Concept-based Explanations for Out-Of-Distribution Detectors
https://github.com/jihyeonseong/cardiocaps
공식 코드CardioCaps: Attention-based Capsule Network for Class-Imbalanced Echocardiogram Classification
https://github.com/jimmyxu123/select
공식 코드SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification
https://github.com/jimzai/mode-ood
공식 코드From Global to Local: Multi-scale Out-of-distribution Detection
https://github.com/jin-s13/gkgnet
공식 코드GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition
https://github.com/jincan333/lot
공식 코드Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate
https://github.com/jindonggu/awesome_adversarial_transferability
공식 코드A Survey on Transferability of Adversarial Examples across Deep Neural Networks
https://github.com/jindongwang/transferlearning
공식 코드https://github.com/jingchensun/prompt_adapter
공식 코드Prompt Tuning based Adapter for Vision-Language Model Adaption
https://github.com/jingjing-nlp/knas
공식 코드https://github.com/jingkang50/oodsurvey
공식 코드https://github.com/jingkang50/openood
공식 코드https://github.com/jingweimo/food-image-classification-
공식 코드Food Image Recognition by Using Convolutional Neural Networks (CNNs)
https://github.com/jingwu321/scissorhands
공식 코드Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks
https://github.com/jingyangxiang/maxq
공식 코드https://github.com/jingyi0000/bimem
공식 코드Black-box Unsupervised Domain Adaptation with Bi-directional Atkinson-Shiffrin Memory
https://github.com/jinxins/adversarial-automixup
공식 코드https://github.com/jinxu06/metafun-tensorflow
공식 코드https://github.com/jinyyy666/mm-bp-snn
공식 코드Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks
https://github.com/jiyounglee-0523/visalign
공식 코드VisAlign: Dataset for Measuring the Degree of Alignment between AI and Humans in Visual Perception
https://github.com/jizongFox/Self-paced-Contrastive-Learning
공식 코드Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels
https://github.com/jjfeng/bc-llm
공식 코드https://github.com/jjongyn/cml
공식 코드https://github.com/jkatzsam/woods_ood
공식 코드https://github.com/jlezama/OrthogonalLowrankEmbedding
공식 코드OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning
https://github.com/jluij/anchored_representation_clouds
공식 코드ARC: Anchored Representation Clouds for High-Resolution INR Classification
https://github.com/jmaronas/DecoupledBayesianCalibration.pytorch
공식 코드Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.