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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/openvinotoolkit/training_extensions
공식 코드ASL Recognition with Metric-Learning based Lightweight Network
https://github.com/optimization-ai/icml2023_ldr
공식 코드https://github.com/optml-group/bibaddiff
공식 코드From Trojan Horses to Castle Walls: Unveiling Bilateral Data Poisoning Effects in Diffusion Models
https://github.com/optml-group/unlearn-saliency
공식 코드https://github.com/orailix/pact
공식 코드PACT: Pruning and Clustering-Based Token Reduction for Faster Visual Language Models
https://github.com/orion-ai-lab/efficientbigearthnet
공식 코드Benchmarking and scaling of deep learning models for land cover image classification
https://github.com/ornl/aadl
공식 코드https://github.com/ortonwang/pcdal
공식 코드https://github.com/oryany12/DeSparsify-Adversarial-Attack
공식 코드DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision Transformers
https://github.com/osamazeeshan/subject-based-domain-adaptation-for-fer
공식 코드Subject-Based Domain Adaptation for Facial Expression Recognition
https://github.com/oscarkey/interlocking-backprop
공식 코드Interlocking Backpropagation: Improving depthwise model-parallelism
https://github.com/osilly/kdfs
공식 코드Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler
https://github.com/osimeoni/RethinkingDeepActiveLearning
공식 코드Rethinking deep active learning: Using unlabeled data at model training
https://github.com/oskyhn/CNNs-Without-Borders
공식 코드On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
https://github.com/ostadabbas/ScanAvaGenerationToolkit
공식 코드A Semi-Supervised Data Augmentation Approach using 3D Graphical Engines
https://github.com/osu-nlp-group/cosmo
공식 코드https://github.com/osvai/af-dcd
공식 코드Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation
https://github.com/osvai/noah
공식 코드NOAH: Learning Pairwise Object Category Attentions for Image Classification
https://github.com/osvai/ssd-kd
공식 코드https://github.com/oswaldoludwig/Eigenvalue-Decay-Regularizer-for-Keras
공식 코드https://github.com/oucailab/msfmamba
공식 코드https://github.com/oustudent/neuraloptimizersearch
공식 코드Neural Optimizer Equation, Decay Function, and Learning Rate Schedule Joint Evolution
https://github.com/outsiders17711/e2eet-skeleton-based-hgr-using-data-level-fusion
공식 코드Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN
https://github.com/ouyanghaodong/deyo
공식 코드https://github.com/oveilleux/realistic_transductive_few_shot
공식 코드https://github.com/ozansener/active_learning_coreset
공식 코드Active Learning for Convolutional Neural Networks: A Core-Set Approach
https://github.com/p-lambda/jukemir
공식 코드Codified audio language modeling learns useful representations for music information retrieval
https://github.com/p-lambda/wilds
공식 코드https://github.com/paarthneekhara/multimodal_rerprogramming
공식 코드https://github.com/pachecobeto95/distortion_robust_dnns_with_early_exit
공식 코드Early-exit deep neural networks for distorted images: providing an efficient edge offloading
https://github.com/padeler/pe-former
공식 코드https://github.com/pairlab/CBS
공식 코드https://github.com/palchenli/TVD
공식 코드PTVD: A Large-Scale Plot-Oriented Multimodal Dataset Based on Television Dramas
https://github.com/pallgeuer/novic
공식 코드https://github.com/pallgeuer/object_noun_dictionary
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.