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The abundance of user-generated online content has presented significant challenges in handling big data. One challenge involves analyzing short posts on social media, ranging from sentiment identification to abusive content detection. Despite recent advancements in pre-trained language models and transfer learning for textual data analysis, the classification performance is hindered by imbalanced data, where anomalous data represents only a small portion of the dataset. To address this, we propose Deep One-Class Fine-Tuning (DOCFT), a versatile method for fine-tuning transfer learning-based t...

Every Friday at Oxen.ai we host a paper club called "Arxiv Dives" to make us smarter Oxen �� ��. We believe diving into the details of research papers is the best way to build fundamental knowledge and keep up with the bleeding edge. If you would like to join the discussion

This guide provides an explanation of AI transfer learning from large language models (LLM) offering a comprehensive guide on how it works and

Introducing LexiFuse+: a semi-supervised model merging lexicon features, BERT transfer learning, and one-class classifiers to detect anomalous content in short texts. It tackles challenges of informal text and imbalanced datasets, excelling in hate speech detection. By leveraging one-class classifiers in a fully deep fine-tuned network trained with unlabeled data, LexiFuse+ surpasses base models.

Overstaffing production in underground coal mining is not convenient for daily management, and incomplete information of coal miners hinders the rescue process of firefighters during mine accidents. To address this safety sustainability issue, a novel face recognition method based on an improved multiscale neural network is proposed in this paper. A new depthwise separable (DS)-inception block is designed and a joint supervised loss function based on center loss theory is developed to construct a new multiscale model. The miniers can be recognized in the harsh underground environment during th...