2,806 research outputs found
A Survey on Food Computing
Food is very essential for human life and it is fundamental to the human
experience. Food-related study may support multifarious applications and
services, such as guiding the human behavior, improving the human health and
understanding the culinary culture. With the rapid development of social
networks, mobile networks, and Internet of Things (IoT), people commonly
upload, share, and record food images, recipes, cooking videos, and food
diaries, leading to large-scale food data. Large-scale food data offers rich
knowledge about food and can help tackle many central issues of human society.
Therefore, it is time to group several disparate issues related to food
computing. Food computing acquires and analyzes heterogenous food data from
disparate sources for perception, recognition, retrieval, recommendation, and
monitoring of food. In food computing, computational approaches are applied to
address food related issues in medicine, biology, gastronomy and agronomy. Both
large-scale food data and recent breakthroughs in computer science are
transforming the way we analyze food data. Therefore, vast amounts of work has
been conducted in the food area, targeting different food-oriented tasks and
applications. However, there are very few systematic reviews, which shape this
area well and provide a comprehensive and in-depth summary of current efforts
or detail open problems in this area. In this paper, we formalize food
computing and present such a comprehensive overview of various emerging
concepts, methods, and tasks. We summarize key challenges and future directions
ahead for food computing. This is the first comprehensive survey that targets
the study of computing technology for the food area and also offers a
collection of research studies and technologies to benefit researchers and
practitioners working in different food-related fields.Comment: Accepted by ACM Computing Survey
High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel
We apply a deep convolutional neural network segmentation model to enable
novel automated microstructure segmentation applications for complex
microstructures typically evaluated manually and subjectively. We explore two
microstructure segmentation tasks in an openly-available ultrahigh carbon steel
microstructure dataset: segmenting cementite particles in the spheroidized
matrix, and segmenting larger fields of view featuring grain boundary carbide,
spheroidized particle matrix, particle-free grain boundary denuded zone, and
Widmanst\"atten cementite. We also demonstrate how to combine these data-driven
microstructure segmentation models to obtain empirical cementite particle size
and denuded zone width distributions from more complex micrographs containing
multiple microconstituents. The full annotated dataset is available on
materialsdata.nist.gov (https://materialsdata.nist.gov/handle/11256/964).Comment: Updated with minor revisions reflecting the review process at
Microscopy and Microanalysis. Full supplementary materials will be available
at https://holmgroup.github.io/publications
Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning
Quality control is a fundamental component of many manufacturing processes,
especially those involving casting or welding. However, manual quality control
procedures are often time-consuming and error-prone. In order to meet the
growing demand for high-quality products, the use of intelligent visual
inspection systems is becoming essential in production lines. Recently,
Convolutional Neural Networks (CNNs) have shown outstanding performance in both
image classification and localization tasks. In this article, a system is
proposed for the identification of casting defects in X-ray images, based on
the Mask Region-based CNN architecture. The proposed defect detection system
simultaneously performs defect detection and segmentation on input images,
making it suitable for a range of defect detection tasks. It is shown that
training the network to simultaneously perform defect detection and defect
instance segmentation, results in a higher defect detection accuracy than
training on defect detection alone. Transfer learning is leveraged to reduce
the training data demands and increase the prediction accuracy of the trained
model. More specifically, the model is first trained with two large
openly-available image datasets before finetuning on a relatively small metal
casting X-ray dataset. The accuracy of the trained model exceeds state-of-the
art performance on the GRIMA database of X-ray images (GDXray) Castings dataset
and is fast enough to be used in a production setting. The system also performs
well on the GDXray Welds dataset. A number of in-depth studies are conducted to
explore how transfer learning, multi-task learning, and multi-class learning
influence the performance of the trained system
Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network
In fruit production, critical crop management decisions are guided by bloom intensity, i.e., the number of flowers present in an orchard. Despite its importance, bloom intensity is still typically estimated by means of human visual inspection. Existing automated computer vision systems for flower identification are based on hand-engineered techniques that work only under specific conditions and with limited performance. This letter proposes an automated technique for flower identification that is robust to uncontrolled environments and applicable to different flower species. Our method relies on an end-to-end residual convolutional neural network (CNN) that represents the state-of-the-art in semantic segmentation. To enhance its sensitivity to flowers, we fine-tune this network using a single dataset of apple flower images. Since CNNs tend to produce coarse segmentations, we employ a refinement method to better distinguish between individual flower instances. Without any preprocessing or dataset-specific training, experimental results on images of apple, peach, and pear flowers, acquired under different conditions demonstrate the robustness and broad applicability of our method
Deep Learning for Sentiment Analysis : A Survey
Deep learning has emerged as a powerful machine learning technique that
learns multiple layers of representations or features of the data and produces
state-of-the-art prediction results. Along with the success of deep learning in
many other application domains, deep learning is also popularly used in
sentiment analysis in recent years. This paper first gives an overview of deep
learning and then provides a comprehensive survey of its current applications
in sentiment analysis.Comment: 34 pages, 9 figures, 2 table
HyperSTAR: Task-Aware Hyperparameters for Deep Networks
While deep neural networks excel in solving visual recognition tasks, they
require significant effort to find hyperparameters that make them work
optimally. Hyperparameter Optimization (HPO) approaches have automated the
process of finding good hyperparameters but they do not adapt to a given task
(task-agnostic), making them computationally inefficient. To reduce HPO time,
we present HyperSTAR (System for Task Aware Hyperparameter Recommendation), a
task-aware method to warm-start HPO for deep neural networks. HyperSTAR ranks
and recommends hyperparameters by predicting their performance conditioned on a
joint dataset-hyperparameter space. It learns a dataset (task) representation
along with the performance predictor directly from raw images in an end-to-end
fashion. The recommendations, when integrated with an existing HPO method, make
it task-aware and significantly reduce the time to achieve optimal performance.
We conduct extensive experiments on 10 publicly available large-scale image
classification datasets over two different network architectures, validating
that HyperSTAR evaluates 50% less configurations to achieve the best
performance compared to existing methods. We further demonstrate that HyperSTAR
makes Hyperband (HB) task-aware, achieving the optimal accuracy in just 25% of
the budget required by both vanilla HB and Bayesian Optimized HB~(BOHB).Comment: Published at CVPR 2020 (Oral
"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets
For an unknown (new) classification dataset, choosing an appropriate deep
learning architecture is often a recursive, time-taking, and laborious process.
In this research, we propose a novel technique to recommend a suitable
architecture from a repository of known models. Further, we predict the
performance accuracy of the recommended architecture on the given unknown
dataset, without the need for training the model. We propose a model encoder
approach to learn a fixed length representation of deep learning architectures
along with its hyperparameters, in an unsupervised fashion. We manually curate
a repository of image datasets with corresponding known deep learning models
and show that the predicted accuracy is a good estimator of the actual
accuracy. We discuss the implications of the proposed approach for three
benchmark images datasets and also the challenges in using the approach for
text modality. To further increase the reproducibility of the proposed
approach, the entire implementation is made publicly available along with the
trained models.Comment: NeurIPS 2019, New in ML Grou
Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning
Early fault diagnosis in complex mechanical systems such as gearbox has
always been a great challenge, even with the recent development in deep neural
networks. The performance of a classic fault diagnosis system predominantly
depends on the features extracted and the classifier subsequently applied.
Although a large number of attempts have been made regarding feature extraction
techniques, the methods require great human involvements are heavily depend on
domain expertise and may thus be non-representative and biased from application
to application. On the other hand, while the deep neural networks based
approaches feature adaptive feature extractions and inherent classifications,
they usually require a substantial set of training data and thus hinder their
usage for engineering applications with limited training data such as gearbox
fault diagnosis. This paper develops a deep convolutional neural network-based
transfer learning approach that not only entertains pre-processing free
adaptive feature extractions, but also requires only a small set of training
data. The proposed approach performs gear fault diagnosis using pre-processing
free raw accelerometer data and experiments with various sizes of training data
were conducted. The superiority of the proposed approach is revealed by
comparing the performance with other methods such as locally trained
convolution neural network and angle-frequency analysis based support vector
machine. The achieved accuracy indicates that the proposed approach is not only
viable and robust, but also has the potential to be readily applicable to other
fault diagnosis practices
Yum-me: A Personalized Nutrient-based Meal Recommender System
Nutrient-based meal recommendations have the potential to help individuals
prevent or manage conditions such as diabetes and obesity. However, learning
people's food preferences and making recommendations that simultaneously appeal
to their palate and satisfy nutritional expectations are challenging. Existing
approaches either only learn high-level preferences or require a prolonged
learning period. We propose Yum-me, a personalized nutrient-based meal
recommender system designed to meet individuals' nutritional expectations,
dietary restrictions, and fine-grained food preferences. Yum-me enables a
simple and accurate food preference profiling procedure via a visual quiz-based
user interface, and projects the learned profile into the domain of
nutritionally appropriate food options to find ones that will appeal to the
user. We present the design and implementation of Yum-me, and further describe
and evaluate two innovative contributions. The first contriution is an open
source state-of-the-art food image analysis model, named FoodDist. We
demonstrate FoodDist's superior performance through careful benchmarking and
discuss its applicability across a wide array of dietary applications. The
second contribution is a novel online learning framework that learns food
preference from item-wise and pairwise image comparisons. We evaluate the
framework in a field study of 227 anonymous users and demonstrate that it
outperforms other baselines by a significant margin. We further conducted an
end-to-end validation of the feasibility and effectiveness of Yum-me through a
60-person user study, in which Yum-me improves the recommendation acceptance
rate by 42.63%
Infrastructure for Usable Machine Learning: The Stanford DAWN Project
Despite incredible recent advances in machine learning, building machine
learning applications remains prohibitively time-consuming and expensive for
all but the best-trained, best-funded engineering organizations. This expense
comes not from a need for new and improved statistical models but instead from
a lack of systems and tools for supporting end-to-end machine learning
application development, from data preparation and labeling to
productionization and monitoring. In this document, we outline opportunities
for infrastructure supporting usable, end-to-end machine learning applications
in the context of the nascent DAWN (Data Analytics for What's Next) project at
Stanford
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