16,732 research outputs found
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
Visual multimedia have become an inseparable part of our digital social
lives, and they often capture moments tied with deep affections. Automated
visual sentiment analysis tools can provide a means of extracting the rich
feelings and latent dispositions embedded in these media. In this work, we
explore how Convolutional Neural Networks (CNNs), a now de facto computational
machine learning tool particularly in the area of Computer Vision, can be
specifically applied to the task of visual sentiment prediction. We accomplish
this through fine-tuning experiments using a state-of-the-art CNN and via
rigorous architecture analysis, we present several modifications that lead to
accuracy improvements over prior art on a dataset of images from a popular
social media platform. We additionally present visualizations of local patterns
that the network learned to associate with image sentiment for insight into how
visual positivity (or negativity) is perceived by the model.Comment: Accepted for publication in Image and Vision Computing. Models and
source code available at https://github.com/imatge-upc/sentiment-201
Preserving Differential Privacy in Convolutional Deep Belief Networks
The remarkable development of deep learning in medicine and healthcare domain
presents obvious privacy issues, when deep neural networks are built on users'
personal and highly sensitive data, e.g., clinical records, user profiles,
biomedical images, etc. However, only a few scientific studies on preserving
privacy in deep learning have been conducted. In this paper, we focus on
developing a private convolutional deep belief network (pCDBN), which
essentially is a convolutional deep belief network (CDBN) under differential
privacy. Our main idea of enforcing epsilon-differential privacy is to leverage
the functional mechanism to perturb the energy-based objective functions of
traditional CDBNs, rather than their results. One key contribution of this work
is that we propose the use of Chebyshev expansion to derive the approximate
polynomial representation of objective functions. Our theoretical analysis
shows that we can further derive the sensitivity and error bounds of the
approximate polynomial representation. As a result, preserving differential
privacy in CDBNs is feasible. We applied our model in a health social network,
i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for
human behavior prediction, human behavior classification, and handwriting digit
recognition tasks. Theoretical analysis and rigorous experimental evaluations
show that the pCDBN is highly effective. It significantly outperforms existing
solutions
Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling
Human personality is significantly represented by those words which he/she
uses in his/her speech or writing. As a consequence of spreading the
information infrastructures (specifically the Internet and social media), human
communications have reformed notably from face to face communication.
Generally, Automatic Personality Prediction (or Perception) (APP) is the
automated forecasting of the personality on different types of human
generated/exchanged contents (like text, speech, image, video, etc.). The major
objective of this study is to enhance the accuracy of APP from the text. To
this end, we suggest five new APP methods including term frequency
vector-based, ontology-based, enriched ontology-based, latent semantic analysis
(LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the
base ones, contribute to each other to enhance the APP accuracy through
ensemble modeling (stacking) based on a hierarchical attention network (HAN) as
the meta-model. The results show that ensemble modeling enhances the accuracy
of APP
- …