Unsupervised Learning from Analogue Videos via Meta-Learning – Analogue video data are large data for many applications including social media and social media. In this work, we first investigate the existence of an analogue video dataset which can be used to construct a large dataset of the videos of human activities. We show that a deep convolutional neural network (CNN) can learn to extract and reuse relevant temporal information of the videos. We also show that a deep learning approach that automatically extracts information based on previous frames of the video can be used to model the current moment’s content and thus improve the learnt similarity between different videos in the same video context. We evaluate the proposed approach by a series of quantitative experiments, comparing it to a CNN trained on the real-world videos produced by human action recognition applications. The results show that using an analogue video dataset can lead to the best performance in human actions recognition on four benchmark domains.

In this paper, we propose a neural network classifier for nonuniform recognition. The proposed algorithm for classification consists of three steps. First, to predict a label of a feature vector for a given label vector, the model must be able to learn a vector representation of the feature vector with a regularization term. Second, our algorithm is to minimize an error term that minimizes the loss in the prediction error when the model fails to predict a label vector. Third, the proposed algorithm uses a discriminative loss to learn a discriminative discriminative feature vector with a regularizer term. The discriminative loss learns a representation of features from discriminative features and outputs high accuracy predictions in terms of feature vectors with a regularization term. The output data is also generated for subsequent tasks including sparse prediction, sparse classification and sparse classification. The performance of our method is comparable to state-of-the-art methods and has significantly improved predictions compared to other methods.

Generalist probability theory and dynamic decision support systems

Constrained Multi-View Image Classification with Multi-temporal Deep CNN Regressions

Unsupervised Learning from Analogue Videos via Meta-Learning

Learning and Estimation from Unbalanced Data

A deep learning pancreas segmentation algorithm with cascaded dictionary regularizationIn this paper, we propose a neural network classifier for nonuniform recognition. The proposed algorithm for classification consists of three steps. First, to predict a label of a feature vector for a given label vector, the model must be able to learn a vector representation of the feature vector with a regularization term. Second, our algorithm is to minimize an error term that minimizes the loss in the prediction error when the model fails to predict a label vector. Third, the proposed algorithm uses a discriminative loss to learn a discriminative discriminative feature vector with a regularizer term. The discriminative loss learns a representation of features from discriminative features and outputs high accuracy predictions in terms of feature vectors with a regularization term. The output data is also generated for subsequent tasks including sparse prediction, sparse classification and sparse classification. The performance of our method is comparable to state-of-the-art methods and has significantly improved predictions compared to other methods.