4,047 research outputs found
Hierarchical Attention Network for Visually-aware Food Recommendation
Food recommender systems play an important role in assisting users to
identify the desired food to eat. Deciding what food to eat is a complex and
multi-faceted process, which is influenced by many factors such as the
ingredients, appearance of the recipe, the user's personal preference on food,
and various contexts like what had been eaten in the past meals. In this work,
we formulate the food recommendation problem as predicting user preference on
recipes based on three key factors that determine a user's choice on food,
namely, 1) the user's (and other users') history; 2) the ingredients of a
recipe; and 3) the descriptive image of a recipe. To address this challenging
problem, we develop a dedicated neural network based solution Hierarchical
Attention based Food Recommendation (HAFR) which is capable of: 1) capturing
the collaborative filtering effect like what similar users tend to eat; 2)
inferring a user's preference at the ingredient level; and 3) learning user
preference from the recipe's visual images. To evaluate our proposed method, we
construct a large-scale dataset consisting of millions of ratings from
AllRecipes.com. Extensive experiments show that our method outperforms several
competing recommender solutions like Factorization Machine and Visual Bayesian
Personalized Ranking with an average improvement of 12%, offering promising
results in predicting user preference for food. Codes and dataset will be
released upon acceptance
Personalized Food Image Classification: Benchmark Datasets and New Baseline
Food image classification is a fundamental step of image-based dietary
assessment, enabling automated nutrient analysis from food images. Many current
methods employ deep neural networks to train on generic food image datasets
that do not reflect the dynamism of real-life food consumption patterns, in
which food images appear sequentially over time, reflecting the progression of
what an individual consumes. Personalized food classification aims to address
this problem by training a deep neural network using food images that reflect
the consumption pattern of each individual. However, this problem is
under-explored and there is a lack of benchmark datasets with individualized
food consumption patterns due to the difficulty in data collection. In this
work, we first introduce two benchmark personalized datasets including the
Food101-Personal, which is created based on surveys of daily dietary patterns
from participants in the real world, and the VFNPersonal, which is developed
based on a dietary study. In addition, we propose a new framework for
personalized food image classification by leveraging self-supervised learning
and temporal image feature information. Our method is evaluated on both
benchmark datasets and shows improved performance compared to existing works.
The dataset has been made available at:
https://skynet.ecn.purdue.edu/~pan161/dataset_personal.htmlComment: Accepted by IEEE Asilomar conference (2023
Adversarial Attacks on Deep Neural Networks for Time Series Classification
Time Series Classification (TSC) problems are encountered in many real life
data mining tasks ranging from medicine and security to human activity
recognition and food safety. With the recent success of deep neural networks in
various domains such as computer vision and natural language processing,
researchers started adopting these techniques for solving time series data
mining problems. However, to the best of our knowledge, no previous work has
considered the vulnerability of deep learning models to adversarial time series
examples, which could potentially make them unreliable in situations where the
decision taken by the classifier is crucial such as in medicine and security.
For computer vision problems, such attacks have been shown to be very easy to
perform by altering the image and adding an imperceptible amount of noise to
trick the network into wrongly classifying the input image. Following this line
of work, we propose to leverage existing adversarial attack mechanisms to add a
special noise to the input time series in order to decrease the network's
confidence when classifying instances at test time. Our results reveal that
current state-of-the-art deep learning time series classifiers are vulnerable
to adversarial attacks which can have major consequences in multiple domains
such as food safety and quality assurance.Comment: Accepted at IJCNN 201
- …