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Found 6 papers, 1 papers with code
Credal Learning Theory
Statistical learning theory is the foundation of machine learning, providing theoretical bounds for the risk of models learnt from a (single) training set, assumed to issue from an unknown probability distribution.
Self-Distilled Vision Transformer for Domain Generalization
Similar to CNNs, ViTs also struggle in out-of-distribution scenarios and the main culprit is overfitting to source domains.
Illumination Invariant Foreground Object Segmentation using ForeGANs
To address this problem, our presented GAN model is trained on background image samples with dynamic changes, after that for testing the GAN model has to generate the same background sample as test sample with similar conditions via back-propagation technique.
Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation
Currently, the top current background subtraction methods in CDnet 2014 are based on deep neural networks with a large gap of performance in comparison on the conventional unsupervised approaches based on multi-features or multi-cues strategies.
Unsupervised RGBD Video Object Segmentation Using GANs
To handle these challenges we propose a fusion based moving object segmentation algorithm which exploits color as well as depth information using GAN to achieve more accuracy.
Unsupervised Deep Context Prediction for Background Foreground Separation
Furthermore we also evaluated foreground object detection with the fusion of our proposed method and morphological operations.
