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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/kekeblom/DeepCGP
공식 코드https://github.com/kelenlv/learn2pfed
공식 코드https://github.com/kellygong/camilla
공식 코드Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms
https://github.com/kennthshang/phavip
공식 코드https://github.com/kenny-co/sgd-uap-torch
공식 코드Universal Adversarial Robustness of Texture and Shape-Biased Models
https://github.com/kentaroy47/vision-transformers-cifar10
공식 코드Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization
https://github.com/keunwoochoi/Auralisation
공식 코드Explaining Deep Convolutional Neural Networks on Music Classification
https://github.com/kevin-ssy/FishNet
공식 코드FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction
https://github.com/kevin-ssy/ViP
공식 코드Visual Parser: Representing Part-whole Hierarchies with Transformers
https://github.com/keyang8/witt
공식 코드WITT: A Wireless Image Transmission Transformer for Semantic Communications
https://github.com/keyu-tian/spark
공식 코드Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling
https://github.com/khaleelkhan/evnn
공식 코드Efficient recurrent architectures through activity sparsity and sparse back-propagation through time
https://github.com/khy0809/fewshot-egnn
공식 코드https://github.com/kiananvari/mtms-a-domain-adaptive-meta-knowledge-distillation-model-for-medical-image-classification
공식 코드https://github.com/kihyukyoon/layeract
공식 코드LayerAct: Advanced Activation Mechanism for Robust Inference of CNNs
https://github.com/kimiyoung/ssl_bad_gan
공식 코드https://github.com/kingjamessong/rankfeat
공식 코드RankFeat&RankWeight: Rank-1 Feature/Weight Removal for Out-of-distribution Detection
https://github.com/kinredon/umt
공식 코드https://github.com/kiraving/SegGradCAM
공식 코드Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mapping
https://github.com/kirk86/RAIOOD
공식 코드On the Importance of Regularisation & Auxiliary Information in OOD Detection
https://github.com/kiryor/nnPUlearning
공식 코드Generating Relevant Counter-Examples from a Positive Unlabeled Dataset for Image Classification
https://github.com/kisnikser/landscape-hessian
공식 코드Unraveling the Hessian: A Key to Smooth Convergence in Loss Function Landscapes
https://github.com/kiyan-rezaee/systematic-literature-review-on-online-continual-learning
공식 코드Online Continual Learning: A Systematic Literature Review of Approaches, Challenges, and Benchmarks
https://github.com/kjunelee/MetaOptNet
공식 코드https://github.com/kk7nc/RMDL
공식 코드https://github.com/kkalais/stochlwta-ml
공식 코드Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning
https://github.com/kkanshul/Hide-and-Seek
공식 코드Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond
https://github.com/kkkcx/diffender
공식 코드DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks
https://github.com/kkonstantinidis/ByzShield
공식 코드ByzShield: An Efficient and Robust System for Distributed Training
https://github.com/klauscc/dam
공식 코드DAM: Dynamic Adapter Merging for Continual Video QA Learning
https://github.com/klayand/pcka
공식 코드Rethinking Centered Kernel Alignment in Knowledge Distillation
https://github.com/klightz/Firefly
공식 코드Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks
https://github.com/kmaag/adversarial-attack-detection-uncertainty
공식 코드Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation
https://github.com/kmbmjn/DropoutCorrectly
공식 코드How to Use Dropout Correctly on Residual Networks with Batch Normalization
https://github.com/kmbmjn/GaussianAttentionBias
공식 코드Understanding Gaussian Attention Bias of Vision Transformers Using Effective Receptive Fields
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.