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arXiv:2406.16925v1 Announce Type: new Abstract: This project investigates the behavior of multi-head attention in Transformer models, specifically focusing on the differences between benign and trojan models in the context of sentiment analysis. Trojan attacks cause models to perform normally on clean inputs but exhibit misclassifications when presented with inputs containing predefined triggers. We characterize attention head functions in trojan and benign models, identifying specific 'trojan' heads and analyzing their behavior. Source link lol

[Submitted on 21 Jun 2024] View a PDF of the paper titled Anime Popularity Prediction Before Huge Investments: a Multimodal Approach Using Deep Learning, by Jes'us Armenta-Segura and 1 other authors View PDF HTML (experimental) Abstract:In the japanese anime industry, predicting whether an upcoming product will be popular is crucial. This paper presents a dataset and methods on predicting anime popularity using a multimodal textimage dataset constructed exclusively from freely available internet sources. The dataset was built following rigorous standards based on real-life investment experiences. A deep neural network architecture leveraging GPT-2 and ResNet-50 to embed the data was employed…

arXiv:2406.17100v1 Announce Type: new Abstract: Diffusion models (DMs) have achieved significant success in generating imaginative images given textual descriptions. However, they are likely to fall short when it comes to real-life scenarios with intricate details.The low-quality, unrealistic human faces in text-to-image generation are one of the most prominent issues, hindering the wide application of DMs in practice. Targeting addressing such an issue, we first assess the face quality of generations from popular pre-trained DMs with the aid of human annotators and then evaluate the alignment between existing metrics such as ImageReward, Human Preference Score, Aesthetic Score Predictor, and Face Quality…