https://github.com/MakeDirtyCode/DC-GAN-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Papers with Code 아카이브
Image Classification
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- 평가표
- 177
- 보존된 평가 기록
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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/MakeDirtyCode/DCGAN-celebA-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MakeDirtyCode/cDCGAN-celebA-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MalayAgarwal-Lee/MesoNet-DeepFakeDetection
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
https://github.com/MalayAgarwal-Lee/SteeringWheelAnglePredictor
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
https://github.com/MaliParag/ScanSSD
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Maligetzus/Semantic-Segmentation-of-Aerial-Imagery
Efficient Ladder-style DenseNets for Semantic Segmentation of Large Images
https://github.com/Maligetzus/Semantic-Segmentation-of-Aerial-Photographs
Efficient Ladder-style DenseNets for Semantic Segmentation of Large Images
https://github.com/Manuscrit/OctaveConv_PlugAndPlay
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/MarSaKi/nasnet
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/MarcAntoineAlex/darts
https://github.com/MarcoSaku/Spiking-C3D
https://github.com/MariefW/6.-trainingYOLO
https://github.com/Marior87/Telegram_Bot
https://github.com/Marius-Juston/AutoCNN
Automatically designing CNN architectures using genetic algorithm for image classification
https://github.com/Mariya1285/Codeathon
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/MarkHershey/arxiv-dl
https://github.com/Markin-Wang/MGANet
Mask Guided Attention For Fine-Grained Patchy Image Classification
https://github.com/Markin-Wang/Mask-Guided-Attention-for-Fine-grained-Patchy-Image-Classification
Mask Guided Attention For Fine-Grained Patchy Image Classification
https://github.com/MartinGer/Attention-Augmented-Convolutional-Networks
https://github.com/MartinGer/Bottleneck-Transformers-for-Visual-Recognition
https://github.com/MartinGer/Stand-Alone-Axial-Attention
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
https://github.com/MarvinLer/SparseConvMIL
https://github.com/MarvinMartin24/MADA-PL
https://github.com/Masao-Taketani/FOTS_OCR
https://github.com/Maskify/darknet
https://github.com/MasoumehVahedi/GANs-Model
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Masquerade51256/DCGAN_SignPicture
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MathiasStensrud/autoBleep
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MathiasStensrud/capstone-2
https://github.com/MatveyMor/substitute_boundary_attack
Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks
https://github.com/Maveric4/pr19aaw01
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MaxHolmberg96/GradCAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/MaxLikesMath/Barlow-Twins-Pytorch
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Fast_and_Accurate_Deep_Network_Learning_by_Exponential_Linear_Units_ELUs
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Improved_Techniques_for_Training_GANs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.