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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/abdulvahapmutlu/vit-protonet
공식 코드ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation
https://github.com/abdurrahman1828/afos
공식 코드Activation Function Optimization Scheme for Image Classification
https://github.com/abheesht17/super-pixels
공식 코드Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification
https://github.com/abhimanyudubey/confusion
공식 코드https://github.com/aboulch/sharesnet
공식 코드ShaResNet: reducing residual network parameter number by sharing weights
https://github.com/abrain-one/nn-dataset
공식 코드https://github.com/abrain-one/nn-plots
공식 코드https://github.com/abrain-one/nn-vr
공식 코드https://github.com/abtahimajeed/checkerboarddenoiser
공식 코드https://github.com/ac-rodriguez/privilegedpooling
공식 코드https://github.com/ac20/TripletWatershed_Code
공식 코드https://github.com/acfr/lbdn
공식 코드https://github.com/achaman2/truly_shift_invariant_cnns
공식 코드Truly shift-equivariant convolutional neural networks with adaptive polyphase upsampling
https://github.com/ackesnal/gtp-vit
공식 코드GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation
https://github.com/acmi-lab/chils
공식 코드CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets
https://github.com/adam2392/morf-demo
공식 코드Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks
https://github.com/adamdad/repfusion
공식 코드https://github.com/adamtupper/ultrasound-augmentation
공식 코드https://github.com/adap/flower
공식 코드https://github.com/adaptivemotorcontrollab/aros
공식 코드Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings
https://github.com/adaptivetokensampling/ATS
공식 코드https://github.com/aderonhuang/fgvc9_snakeclef2022
공식 코드Explored An Effective Methodology for Fine-Grained Snake Recognition
https://github.com/adiko1997/revit
공식 코드ReViT: Enhancing Vision Transformers Feature Diversity with Attention Residual Connections
https://github.com/adipandas/one-shot-steel-surfaces
공식 코드One-Shot Recognition of Manufacturing Defects in Steel Surfaces
https://github.com/admantwerp/xaistories
공식 코드Tell Me a Story! Narrative-Driven XAI with Large Language Models
https://github.com/adobe/antialiased-cnns
공식 코드https://github.com/adonis-galaxy/depthclip
공식 코드https://github.com/adrianalbert/urban-environments
공식 코드https://github.com/adrianiordache/mlfd
공식 코드Multi-Level Feature Distillation of Joint Teachers Trained on Distinct Image Datasets
https://github.com/adrianphoulady/weighted-tsetlin-machine-cpp
공식 코드The Weighted Tsetlin Machine: Compressed Representations with Weighted Clauses
https://github.com/adriencourtois/sing
공식 코드https://github.com/advanced-vision-and-learning-lab/2024_v4a_lacunarity_pooling_layer
공식 코드Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis
https://github.com/advanced-vision-and-learning-lab/nehd_nlbp
공식 코드https://github.com/advboxes/AdvBox
공식 코드Transferability of Adversarial Examples to Attack Cloud-based Image Classifier Service
https://github.com/adverml/spectraldef_framework
공식 코드Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.