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/litianliu/nci-ood
공식 코드Detecting Out-of-Distribution Through the Lens of Neural Collapse
https://github.com/liudongyang6/fcfd
공식 코드https://github.com/liulu112601/Gated-Propagation-Net
공식 코드https://github.com/liulu112601/URT
공식 코드A Universal Representation Transformer Layer for Few-Shot Image Classification
https://github.com/liupei101/psemix
공식 코드Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification
https://github.com/liuquande/FedIRM
공식 코드Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching
https://github.com/liuxingbin/dbot
공식 코드https://github.com/liuxuvip/PolSF
공식 코드https://github.com/liuxuvip/Polarimetric-Scattering-Coding
공식 코드Polarimetric Convolutional Network for PolSAR Image Classification
https://github.com/liuyao12/ConvNets-PDE-perspective
공식 코드A Novel Convolutional Neural Network Architecture with a Continuous Symmetry
https://github.com/liuyuan-wen/av-ood-gzsl
공식 코드Out-Of-Distribution Detection for Audio-visual Generalized Zero-Shot Learning: A General Framework
https://github.com/liuzhuang13/DenseNet
공식 코드https://github.com/liuzhuang13/anytime
공식 코드https://github.com/liuzhuang13/slimming
공식 코드Learning Efficient Convolutional Networks through Network Slimming
https://github.com/liuziyu77/visual-rft
공식 코드https://github.com/living-with-machines/mapreader
공식 코드MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at Scale
https://github.com/lixiaothu/laat
공식 코드Language-Driven Anchors for Zero-Shot Adversarial Robustness
https://github.com/lixiaotong97/dsu
공식 코드https://github.com/lixiaotong97/mc-beit
공식 코드mc-BEiT: Multi-choice Discretization for Image BERT Pre-training
https://github.com/liyi01827/noisy-contrastive
공식 코드On Learning Contrastive Representations for Learning with Noisy Labels
https://github.com/liyunyu08/ReMarNet
공식 코드ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification
https://github.com/ljjcoder/PCL
공식 코드https://github.com/ljjcoder/probabilistic-contrastive-learning
공식 코드https://github.com/lkyddd/gradma
공식 코드https://github.com/llnl/amp
공식 코드https://github.com/llnl/xnas
공식 코드Learning Interpretable Models Through Multi-Objective Neural Architecture Search
https://github.com/lluckyyh/vt-ada
공식 코드https://github.com/lluisgomez/TextTopicNet
공식 코드https://github.com/llyxc/ada-abc
공식 코드Medical Image Debiasing by Learning Adaptive Agreement from a Biased Council
https://github.com/lmb-freiburg/diffusion-for-ood
공식 코드Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond
https://github.com/lmbxmu/DCFF
공식 코드Training Compact CNNs for Image Classification using Dynamic-coded Filter Fusion
https://github.com/lmbxmu/clr-rnf
공식 코드Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters
https://github.com/lmietkiewicz/the-skin-game-research-paper
공식 코드The Skin Game: Revolutionizing Standards for AI Dermatology Model Comparison
https://github.com/lmmmeng/overlock
공식 코드OverLoCK: An Overview-first-Look-Closely-next ConvNet with Context-Mixing Dynamic Kernels
https://github.com/lmmmeng/sparx
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.