24,448 research outputs found
NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection
The labeling cost of large number of bounding boxes is one of the main
challenges for training modern object detectors. To reduce the dependence on
expensive bounding box annotations, we propose a new semi-supervised object
detection formulation, in which a few seed box level annotations and a large
scale of image level annotations are used to train the detector. We adopt a
training-mining framework, which is widely used in weakly supervised object
detection tasks. However, the mining process inherently introduces various
kinds of labelling noises: false negatives, false positives and inaccurate
boundaries, which can be harmful for training the standard object detectors
(e.g. Faster RCNN). We propose a novel NOise Tolerant Ensemble RCNN (NOTE-RCNN)
object detector to handle such noisy labels. Comparing to standard Faster RCNN,
it contains three highlights: an ensemble of two classification heads and a
distillation head to avoid overfitting on noisy labels and improve the mining
precision, masking the negative sample loss in box predictor to avoid the harm
of false negative labels, and training box regression head only on seed
annotations to eliminate the harm from inaccurate boundaries of mined bounding
boxes. We evaluate the methods on ILSVRC 2013 and MSCOCO 2017 dataset; we
observe that the detection accuracy consistently improves as we iterate between
mining and training steps, and state-of-the-art performance is achieved.Comment: 8 page
Robust and Efficient Boosting Method using the Conditional Risk
Well-known for its simplicity and effectiveness in classification, AdaBoost,
however, suffers from overfitting when class-conditional distributions have
significant overlap. Moreover, it is very sensitive to noise that appears in
the labels. This article tackles the above limitations simultaneously via
optimizing a modified loss function (i.e., the conditional risk). The proposed
approach has the following two advantages. (1) It is able to directly take into
account label uncertainty with an associated label confidence. (2) It
introduces a "trustworthiness" measure on training samples via the Bayesian
risk rule, and hence the resulting classifier tends to have finite sample
performance that is superior to that of the original AdaBoost when there is a
large overlap between class conditional distributions. Theoretical properties
of the proposed method are investigated. Extensive experimental results using
synthetic data and real-world data sets from UCI machine learning repository
are provided. The empirical study shows the high competitiveness of the
proposed method in predication accuracy and robustness when compared with the
original AdaBoost and several existing robust AdaBoost algorithms.Comment: 14 Pages, 2 figures and 5 table
Error-Resilient Machine Learning in Near Threshold Voltage via Classifier Ensemble
In this paper, we present the design of error-resilient machine learning
architectures by employing a distributed machine learning framework referred to
as classifier ensemble (CE). CE combines several simple classifiers to obtain a
strong one. In contrast, centralized machine learning employs a single complex
block. We compare the random forest (RF) and the support vector machine (SVM),
which are representative techniques from the CE and centralized frameworks,
respectively. Employing the dataset from UCI machine learning repository and
architectural-level error models in a commercial 45 nm CMOS process, it is
demonstrated that RF-based architectures are significantly more robust than SVM
architectures in presence of timing errors due to process variations in
near-threshold voltage (NTV) regions (0.3 V - 0.7 V). In particular, the RF
architecture exhibits a detection accuracy (P_{det}) that varies by 3.2% while
maintaining a median P_{det} > 0.9 at a gate level delay variation of 28.9% .
In comparison, SVM exhibits a P_{det} that varies by 16.8%. Additionally, we
propose an error weighted voting technique that incorporates the timing error
statistics of the NTV circuit fabric to further enhance robustness. Simulation
results confirm that the error weighted voting achieves a P_{det} that varies
by only 1.4%, which is 12X lower compared to SVM
Robust Visual Tracking Using Dynamic Classifier Selection with Sparse Representation of Label Noise
Recently a category of tracking methods based on "tracking-by-detection" is
widely used in visual tracking problem. Most of these methods update the
classifier online using the samples generated by the tracker to handle the
appearance changes. However, the self-updating scheme makes these methods
suffer from drifting problem because of the incorrect labels of weak
classifiers in training samples. In this paper, we split the class labels into
true labels and noise labels and model them by sparse representation. A novel
dynamic classifier selection method, robust to noisy training data, is
proposed. Moreover, we apply the proposed classifier selection algorithm to
visual tracking by integrating a part based online boosting framework. We have
evaluated our proposed method on 12 challenging sequences involving severe
occlusions, significant illumination changes and large pose variations. Both
the qualitative and quantitative evaluations demonstrate that our approach
tracks objects accurately and robustly and outperforms state-of-the-art
trackers.Comment: accepted at ACCV2012, Ora
A multi-instance learning algorithm based on a stacked ensemble of lazy learners
This document describes a novel learning algorithm that classifies "bags" of
instances rather than individual instances. A bag is labeled positive if it
contains at least one positive instance (which may or may not be specifically
identified), and negative otherwise. This class of problems is known as
multi-instance learning problems, and is useful in situations where the class
label at an instance level may be unavailable or imprecise or difficult to
obtain, or in situations where the problem is naturally posed as one of
classifying instance groups. The algorithm described here is an ensemble-based
method, wherein the members of the ensemble are lazy learning classifiers
learnt using the Citation Nearest Neighbour method. Diversity among the
ensemble members is achieved by optimizing their parameters using a
multi-objective optimization method, with the objectives being to maximize
Class 1 accuracy and minimize false positive rate. The method has been found to
be effective on the Musk1 benchmark dataset
Hyperbox based machine learning algorithms: A comprehensive survey
With the rapid development of digital information, the data volume generated
by humans and machines is growing exponentially. Along with this trend, machine
learning algorithms have been formed and evolved continuously to discover new
information and knowledge from different data sources. Learning algorithms
using hyperboxes as fundamental representational and building blocks are a
branch of machine learning methods. These algorithms have enormous potential
for high scalability and online adaptation of predictors built using hyperbox
data representations to the dynamically changing environments and streaming
data. This paper aims to give a comprehensive survey of literature on
hyperbox-based machine learning models. In general, according to the
architecture and characteristic features of the resulting models, the existing
hyperbox-based learning algorithms may be grouped into three major categories:
fuzzy min-max neural networks, hyperbox-based hybrid models, and other
algorithms based on hyperbox representations. Within each of these groups, this
paper shows a brief description of the structure of models, associated learning
algorithms, and an analysis of their advantages and drawbacks. Main
applications of these hyperbox-based models to the real-world problems are also
described in this paper. Finally, we discuss some open problems and identify
potential future research directions in this field.Comment: 7 figure
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
We describe a method for visual object detection based on an ensemble of
optimized decision trees organized in a cascade of rejectors. The trees use
pixel intensity comparisons in their internal nodes and this makes them able to
process image regions very fast. Experimental analysis is provided through a
face detection problem. The obtained results are encouraging and demonstrate
that the method has practical value. Additionally, we analyse its sensitivity
to noise and show how to perform fast rotation invariant object detection.
Complete source code is provided at https://github.com/nenadmarkus/pico
Hierarchical Pooling Structure for Weakly Labeled Sound Event Detection
Sound event detection with weakly labeled data is considered as a problem of
multi-instance learning. And the choice of pooling function is the key to
solving this problem. In this paper, we proposed a hierarchical pooling
structure to improve the performance of weakly labeled sound event detection
system. Proposed pooling structure has made remarkable improvements on three
types of pooling function without adding any parameters. Moreover, our system
has achieved competitive performance on Task 4 of Detection and Classification
of Acoustic Scenes and Events (DCASE) 2017 Challenge using hierarchical pooling
structure
Image Segmentation Using Hierarchical Merge Tree
This paper investigates one of the most fundamental computer vision problems:
image segmentation. We propose a supervised hierarchical approach to
object-independent image segmentation. Starting with over-segmenting
superpixels, we use a tree structure to represent the hierarchy of region
merging, by which we reduce the problem of segmenting image regions to finding
a set of label assignment to tree nodes. We formulate the tree structure as a
constrained conditional model to associate region merging with likelihoods
predicted using an ensemble boundary classifier. Final segmentations can then
be inferred by finding globally optimal solutions to the model efficiently. We
also present an iterative training and testing algorithm that generates various
tree structures and combines them to emphasize accurate boundaries by
segmentation accumulation. Experiment results and comparisons with other very
recent methods on six public data sets demonstrate that our approach achieves
the state-of-the-art region accuracy and is very competitive in image
segmentation without semantic priors
Label Noise Filtering Techniques to Improve Monotonic Classification
The monotonic ordinal classification has increased the interest of
researchers and practitioners within machine learning community in the last
years. In real applications, the problems with monotonicity constraints are
very frequent. To construct predictive monotone models from those problems,
many classifiers require as input a data set satisfying the monotonicity
relationships among all samples. Changing the class labels of the data set
(relabelling) is useful for this. Relabelling is assumed to be an important
building block for the construction of monotone classifiers and it is proved
that it can improve the predictive performance.
In this paper, we will address the construction of monotone datasets
considering as noise the cases that do not meet the monotonicity restrictions.
For the first time in the specialized literature, we propose the use of noise
filtering algorithms in a preprocessing stage with a double goal: to increase
both the monotonicity index of the models and the accuracy of the predictions
for different monotonic classifiers. The experiments are performed over 12
datasets coming from classification and regression problems and show that our
scheme improves the prediction capabilities of the monotonic classifiers
instead of being applied to original and relabeled datasets. In addition, we
have included the analysis of noise filtering process in the particular case of
wine quality classification to understand its effect in the predictive models
generated.Comment: This paper is already accepted for publication in Neurocomputin
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