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/gan3sh500/octaveconv-pytorch
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/ganguli-lab/Synaptic-Flow
SNIP: Single-shot Network Pruning based on Connection Sensitivity
https://github.com/ganjinzero/rrhf
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
https://github.com/gaohuang/MSDNet
Multi-Scale Dense Networks for Resource Efficient Image Classification
https://github.com/gaozhengqing/unient
Towards Open-Set Test-Time Adaptation Utilizing the Wisdom of Crowds in Entropy Minimization
https://github.com/gargarchit/60DaysOfUdacity
https://github.com/garrettruss/ImageRecognitionReact
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/gasvn/Res2Net
https://github.com/gatech-eic/s3-router
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/gatech-eic/vitcod
Training data-efficient image transformers & distillation through attention
https://github.com/gatheluck/Stronghold
https://github.com/gaur-pankaj/Covid19-Detection
Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
https://github.com/gauthiercler/adversarial-mnist
https://github.com/gbarello/CPCLibriSpeech
https://github.com/gbup-group/EAN-efficient-attention-network
https://github.com/gcervantes8/Game-Image-Generator
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/gchaperon/alexnet
https://github.com/gcr/torch-residual-networks
https://github.com/gcucurull/maml_flax
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/gcwl/pytorch-dcgan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/gdec-submission/gdec
https://github.com/gdialektakis/Statistical-Dialogue-Systems-with-Adversarial-AutoEncoders
https://github.com/gdvorsak97/tmp_OSPD_copy
https://github.com/gdwang08/STFPM
Student-Teacher Feature Pyramid Matching for Anomaly Detection
https://github.com/gebob19/REPTILE-Metalearning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/gebob19/cscd94-metalearning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/gebob19/cscd94_metalearning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/gebob19/natural-policy-gradient-reinforcement-learning
TENGraD: Time-Efficient Natural Gradient Descent with Exact Fisher-Block Inversion
https://github.com/geekswaroop/Human-Parsing
https://github.com/geekysethi/triplet-GAN
https://github.com/general-ken0bi/GAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/genforce/ghfeat
https://github.com/gengchenmai/csp
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations
https://github.com/gengchenmai/space2vec_demo
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells
https://github.com/gengchenmai/sphere2vec
Sphere2Vec: Multi-Scale Representation Learning over a Spherical Surface for Geospatial Predictions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.