We used AI to automatically write research papers like those on arXiv.org and in academic journals. To be clear, the titles and abstracts for these academic papers are not real, they are 100% computer generated:
Deep Learning for Real Detection with Composed-Seq Images Image data have been a major source of error during the past decades. The primary focus of this paper is to develop a robust and practical framework for image retrieval (i.e. the extraction of images from social media). The data collected from social media content of the internet-based web enables to extract relevant features from the images, such as semantic, visual, contextual, language, and textual labels. We show that, although natural language processing (NN) approaches can extract these features without using images, it is not practical for using social networks for this purpose. To address the problem, we propose a deep convolutional neural network (CNN) with feature extraction algorithms, which significantly outperforms the state-of-the-art. This is in accord with the proposed training paradigm, which combines the best techniques from CNNs with image extraction. We illustrate the benefits of the proposed methodology using both synthetic and real data sets, showing that for a given dataset, learning the features is far from the best solution.
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