2,185 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Distinctive-attribute Extraction for Image Captioning
Image captioning, an open research issue, has been evolved with the progress
of deep neural networks. Convolutional neural networks (CNNs) and recurrent
neural networks (RNNs) are employed to compute image features and generate
natural language descriptions in the research. In previous works, a caption
involving semantic description can be generated by applying additional
information into the RNNs. In this approach, we propose a distinctive-attribute
extraction (DaE) which explicitly encourages significant meanings to generate
an accurate caption describing the overall meaning of the image with their
unique situation. Specifically, the captions of training images are analyzed by
term frequency-inverse document frequency (TF-IDF), and the analyzed semantic
information is trained to extract distinctive-attributes for inferring
captions. The proposed scheme is evaluated on a challenge data, and it improves
an objective performance while describing images in more detail.Comment: 14 main pages, 4 supplementary page
Harnessing Deep Learning Techniques for Text Clustering and Document Categorization
This research paper delves into the realm of deep text clustering algorithms with the aim of enhancing the accuracy of document classification. In recent years, the fusion of deep learning techniques and text clustering has shown promise in extracting meaningful patterns and representations from textual data. This paper provides an in-depth exploration of various deep text clustering methodologies, assessing their efficacy in improving document classification accuracy. Delving into the core of deep text clustering, the paper investigates various feature representation techniques, ranging from conventional word embeddings to contextual embeddings furnished by BERT and GPT models.By critically reviewing and comparing these algorithms, we shed light on their strengths, limitations, and potential applications. Through this comprehensive study, we offer insights into the evolving landscape of document analysis and classification, driven by the power of deep text clustering algorithms.Through an original synthesis of existing literature, this research serves as a beacon for researchers and practitioners in harnessing the prowess of deep learning to enhance the accuracy of document classification endeavors
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Visual Question Answering: A Survey of Methods and Datasets
Visual Question Answering (VQA) is a challenging task that has received
increasing attention from both the computer vision and the natural language
processing communities. Given an image and a question in natural language, it
requires reasoning over visual elements of the image and general knowledge to
infer the correct answer. In the first part of this survey, we examine the
state of the art by comparing modern approaches to the problem. We classify
methods by their mechanism to connect the visual and textual modalities. In
particular, we examine the common approach of combining convolutional and
recurrent neural networks to map images and questions to a common feature
space. We also discuss memory-augmented and modular architectures that
interface with structured knowledge bases. In the second part of this survey,
we review the datasets available for training and evaluating VQA systems. The
various datatsets contain questions at different levels of complexity, which
require different capabilities and types of reasoning. We examine in depth the
question/answer pairs from the Visual Genome project, and evaluate the
relevance of the structured annotations of images with scene graphs for VQA.
Finally, we discuss promising future directions for the field, in particular
the connection to structured knowledge bases and the use of natural language
processing models.Comment: 25 page
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Traditional architectures for solving computer vision problems and the degree
of success they enjoyed have been heavily reliant on hand-crafted features.
However, of late, deep learning techniques have offered a compelling
alternative -- that of automatically learning problem-specific features. With
this new paradigm, every problem in computer vision is now being re-examined
from a deep learning perspective. Therefore, it has become important to
understand what kind of deep networks are suitable for a given problem.
Although general surveys of this fast-moving paradigm (i.e. deep-networks)
exist, a survey specific to computer vision is missing. We specifically
consider one form of deep networks widely used in computer vision -
convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN
and then examine the broad variations proposed over time to suit different
applications. We hope that our recipe-style survey will serve as a guide,
particularly for novice practitioners intending to use deep-learning techniques
for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm
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