Multi-label Multi-task Deep Learning for Behavioral Coding

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Deep Extreme Multi-label Learning

Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves 2 possible label sets when the label dimension L is very large, e.g., in millions for Wikipedia labels. This paper is motivated to better explore the label space by building and modeling an explicit labe...

متن کامل

Deep learning for multi-label scene classification

Scene classification is an important topic in computer vision. For similar weather conditions, there are some obstacles for extracting features from outdoor images. In this thesis, I present a novel approach to classify cloudy and sunny weather images. Inspired by recent study of a deep convolutional neural network and the spatial pyramid matching, I generate a model based on the ImageNet datas...

متن کامل

Deep Learning for Multi-label Classification

In multi-label classification, the main focus has been to develop ways of learning the underlying dependencies between labels, and to take advantage of this at classification time. Developing better feature-space representations has been predominantly employed to reduce complexity, e.g., by eliminating non-helpful feature attributes from the input space prior to (or during) training. This is an...

متن کامل

Deep Automated Multi-task Learning

Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims at improving the performance of a primary task, by jointly training on a secondary task. This paper introduces automated tasks, which exploit the sequential nature of the input data, as secondary tasks in an MTL model. We explore next word prediction, next character pr...

متن کامل

Multi-Modal Multi-Task Deep Learning for Autonomous Driving

Several deep learning approaches have been applied to the autonomous driving task, many employing end-toend deep neural networks. Autonomous driving is complex, utilizing multiple behavioral modalities ranging from lane changing to turning and stopping. However, most existing approaches do not factor in the different behavioral modalities of the driving task into the training strategy. This pap...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: IEEE Transactions on Affective Computing

سال: 2019

ISSN: 1949-3045,2371-9850

DOI: 10.1109/taffc.2019.2952113