https://github.com/gordicaleksa/pytorch-gans
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
Contextual Encoder-Decoder Network for Visual Saliency Prediction
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Averaging Weights Leads to Wider Optima and Better Generalization
Very Deep Convolutional Networks for Large-Scale Image Recognition
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
A Simple Framework for Contrastive Learning of Visual Representations
Aggregated Residual Transformations for Deep Neural Networks
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions
On Exact Bit-level Reversible Transformers Without Changing Architectures
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.