This paper presents a method for learning temporal regularity in video sequences using autoencoders with limited supervision. The authors propose two autoencoder-based approaches to learn regular motion patterns from video data. The first approach uses conventional handcrafted spatio-temporal features and a fully connected autoencoder. The second approach employs a fully convolutional autoencoder to learn both local features and classifiers in an end-to-end manner. The model is trained on multiple video datasets and can capture regularities across different datasets. The learned regularity is used for various applications, including anomaly detection, video summarization, and motion prediction. The authors evaluate their methods both qualitatively and quantitatively. Qualitative results show that the model can identify irregular motions and generate regular frames from videos. Quantitative results demonstrate that the model performs competitively on anomaly detection tasks. The model is generalizable across multiple datasets and is not overfit to any single dataset. The paper also discusses the use of autoencoders for learning motion patterns without supervision. It compares the proposed method with existing approaches and shows that the autoencoder-based method is effective in capturing temporal regularity. The authors also present results on various datasets, including the CUHK Avenue, UCSD Pedestrian, and Subway datasets, demonstrating the model's ability to detect abnormal events. The model is trained using a combination of convolutional and deconvolutional layers to preserve spatial information. The authors also discuss the use of data augmentation techniques to increase the size of the training data. The model is evaluated on multiple datasets, showing its effectiveness in detecting anomalies and capturing temporal regularity. The paper concludes that the proposed autoencoder-based method is effective in learning temporal regularity in video sequences and can be applied to various applications, including anomaly detection and motion prediction. The model is generalizable across multiple datasets and is not overfit to any single dataset. The authors also highlight the importance of using autoencoders for learning motion patterns without supervision and show that the proposed method is effective in capturing temporal regularity.This paper presents a method for learning temporal regularity in video sequences using autoencoders with limited supervision. The authors propose two autoencoder-based approaches to learn regular motion patterns from video data. The first approach uses conventional handcrafted spatio-temporal features and a fully connected autoencoder. The second approach employs a fully convolutional autoencoder to learn both local features and classifiers in an end-to-end manner. The model is trained on multiple video datasets and can capture regularities across different datasets. The learned regularity is used for various applications, including anomaly detection, video summarization, and motion prediction. The authors evaluate their methods both qualitatively and quantitatively. Qualitative results show that the model can identify irregular motions and generate regular frames from videos. Quantitative results demonstrate that the model performs competitively on anomaly detection tasks. The model is generalizable across multiple datasets and is not overfit to any single dataset. The paper also discusses the use of autoencoders for learning motion patterns without supervision. It compares the proposed method with existing approaches and shows that the autoencoder-based method is effective in capturing temporal regularity. The authors also present results on various datasets, including the CUHK Avenue, UCSD Pedestrian, and Subway datasets, demonstrating the model's ability to detect abnormal events. The model is trained using a combination of convolutional and deconvolutional layers to preserve spatial information. The authors also discuss the use of data augmentation techniques to increase the size of the training data. The model is evaluated on multiple datasets, showing its effectiveness in detecting anomalies and capturing temporal regularity. The paper concludes that the proposed autoencoder-based method is effective in learning temporal regularity in video sequences and can be applied to various applications, including anomaly detection and motion prediction. The model is generalizable across multiple datasets and is not overfit to any single dataset. The authors also highlight the importance of using autoencoders for learning motion patterns without supervision and show that the proposed method is effective in capturing temporal regularity.