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/NVlabs/unas
https://github.com/NZ99/bottleneck-transformer-flax
https://github.com/NZ99/transformer_in_transformer_flax
https://github.com/Naagar/cornseedsdataset
Automated Seed Quality Testing System using GAN & Active Learning
https://github.com/Nadavc220/DomainAdversarialTrainingOfNeuralNetworks
https://github.com/Nadhila/Explainble-AI
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/NadimKawwa/DCGAN_faces
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/NajibYavari/DD2412
Averaging Weights Leads to Wider Optima and Better Generalization
https://github.com/Namkyeong/BGRL_Pytorch
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/NatGr/Master_Thesis
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications
https://github.com/NathanDuran/Sentence-Encoding-for-DA-Classification
https://github.com/Nathangitlab/Backdoor-Attacks-on-Crowd-Counting
https://github.com/NatiOfir/DeepFaintEdges
Multi-scale Processing of Noisy Images using Edge Preservation Losses
https://github.com/Natooz/MidiTok
MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training
https://github.com/Natsu6767/DCGAN-PyTorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Natsu6767/InfoGAN-PyTorch
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/NaturalHistoryMuseum/semantic-segmentation
https://github.com/NazirNayal8/visual-transformer
Visual Transformers: Token-based Image Representation and Processing for Computer Vision
https://github.com/Neeleshrj/Image-Colorization-API
https://github.com/Neoanarika/CNN-using-Swish
https://github.com/Neoanarika/Searching-for-activation-functions
https://github.com/Neptune-Trojans/GANs
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/NeptuneProjects/RISCluster
https://github.com/NeptuneProjects/RISClusterPT
https://github.com/NeptuneProjects/RISWorkflow
https://github.com/Neronjust2017/BayesianCNN
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
https://github.com/NetoPedro/Universal-Adversarial-Perturbations-Pytorch
DeepFool: a simple and accurate method to fool deep neural networks
https://github.com/NetraInamdar/Deep_Learning_Project
https://github.com/NeurAI-Lab/UniNet
Noisy Concurrent Training for Efficient Learning under Label Noise
https://github.com/Newmu/dcgan_code
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Nguyendat-bit/MLP-Mixer
https://github.com/Nguyendat-bit/MobilenetV1
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/Nguyendat-bit/MobilenetV2
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/NiMlr/pynlqn
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/Nic5472K/FriendsOOGroup_TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.