38,429 research outputs found
Deep Learning Techniques to Improve the Performance of Olive Oil Classification
The olive oil assessment involves the use of a standardized sensory analysis according
to the “panel test” method. However, there is an important interest to design novel
strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry
(MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment
for olive oil classification. It is an essential task in an attempt to get the most robust
model over time and, both to avoid fraud in the price and to know whether it is suitable
for consumption or not. The aim of this paper is to combine chemical techniques and
Deep Learning approaches to automatically classify olive oil samples from two different
harvests in their three corresponding classes: extra virgin olive oil (EVOO), virgin olive oil
(VOO), and lampante olive oil (LOO). Our Deep Learning model is built with 701 samples,
which were obtained from two olive oil campaigns (2014–2015 and 2015–2016). The
data from the two harvests are built from the selection of specific olive oil markers from
the whole spectral fingerprint obtained with GC-IMS method. In order to obtain the
best results we have configured the parameters of our model according to the nature
of the data. The results obtained show that a deep learning approach applied to data
obtained from chemical instrumental techniques is a good method when classifying oil
samples in their corresponding categories, with higher success rates than those obtained
in previous works.Ministerio de Economía y Competitividad TIN2017-88209-C2-2-
Playing for Data: Ground Truth from Computer Games
Recent progress in computer vision has been driven by high-capacity models
trained on large datasets. Unfortunately, creating large datasets with
pixel-level labels has been extremely costly due to the amount of human effort
required. In this paper, we present an approach to rapidly creating
pixel-accurate semantic label maps for images extracted from modern computer
games. Although the source code and the internal operation of commercial games
are inaccessible, we show that associations between image patches can be
reconstructed from the communication between the game and the graphics
hardware. This enables rapid propagation of semantic labels within and across
images synthesized by the game, with no access to the source code or the
content. We validate the presented approach by producing dense pixel-level
semantic annotations for 25 thousand images synthesized by a photorealistic
open-world computer game. Experiments on semantic segmentation datasets show
that using the acquired data to supplement real-world images significantly
increases accuracy and that the acquired data enables reducing the amount of
hand-labeled real-world data: models trained with game data and just 1/3 of the
CamVid training set outperform models trained on the complete CamVid training
set.Comment: Accepted to the 14th European Conference on Computer Vision (ECCV
2016
Hyperparameter Importance Across Datasets
With the advent of automated machine learning, automated hyperparameter
optimization methods are by now routinely used in data mining. However, this
progress is not yet matched by equal progress on automatic analyses that yield
information beyond performance-optimizing hyperparameter settings. In this
work, we aim to answer the following two questions: Given an algorithm, what
are generally its most important hyperparameters, and what are typically good
values for these? We present methodology and a framework to answer these
questions based on meta-learning across many datasets. We apply this
methodology using the experimental meta-data available on OpenML to determine
the most important hyperparameters of support vector machines, random forests
and Adaboost, and to infer priors for all their hyperparameters. The results,
obtained fully automatically, provide a quantitative basis to focus efforts in
both manual algorithm design and in automated hyperparameter optimization. The
conducted experiments confirm that the hyperparameters selected by the proposed
method are indeed the most important ones and that the obtained priors also
lead to statistically significant improvements in hyperparameter optimization.Comment: \c{opyright} 2018. Copyright is held by the owner/author(s).
Publication rights licensed to ACM. This is the author's version of the work.
It is posted here for your personal use, not for redistribution. The
definitive Version of Record was published in Proceedings of the 24th ACM
SIGKDD International Conference on Knowledge Discovery & Data Minin
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