12,067 research outputs found
Augmented Data as an Auxiliary Plug-in Towards Categorization of Crowdsourced Heritage Data
In this paper, we propose a strategy to mitigate the problem of inefficient
clustering performance by introducing data augmentation as an auxiliary
plug-in. Classical clustering techniques such as K-means, Gaussian mixture
model and spectral clustering are central to many data-driven applications.
However, recently unsupervised simultaneous feature learning and clustering
using neural networks also known as Deep Embedded Clustering (DEC) has gained
prominence. Pioneering works on deep feature clustering focus on defining
relevant clustering loss function and choosing the right neural network for
extracting features. A central problem in all these cases is data sparsity
accompanied by high intra-class and low inter-class variance, which
subsequently leads to poor clustering performance and erroneous candidate
assignments. Towards this, we employ data augmentation techniques to improve
the density of the clusters, thus improving the overall performance. We train a
variant of Convolutional Autoencoder (CAE) with augmented data to construct the
initial feature space as a novel model for deep clustering. We demonstrate the
results of proposed strategy on crowdsourced Indian Heritage dataset. Extensive
experiments show consistent improvements over existing works
Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction
In unsupervised learning, there is no apparent straightforward cost function
that can capture the significant factors of variations and similarities. Since
natural systems have smooth dynamics, an opportunity is lost if an unsupervised
objective function remains static during the training process. The absence of
concrete supervision suggests that smooth dynamics should be integrated.
Compared to classical static cost functions, dynamic objective functions allow
to better make use of the gradual and uncertain knowledge acquired through
pseudo-supervision. In this paper, we propose Dynamic Autoencoder (DynAE), a
novel model for deep clustering that overcomes a clustering-reconstruction
trade-off, by gradually and smoothly eliminating the reconstruction objective
function in favor of a construction one. Experimental evaluations on benchmark
datasets show that our approach achieves state-of-the-art results compared to
the most relevant deep clustering methods
Deep Representation Learning Characterized by Inter-class Separation for Image Clustering
Despite significant advances in clustering methods in recent years, the
outcome of clustering of a natural image dataset is still unsatisfactory due to
two important drawbacks. Firstly, clustering of images needs a good feature
representation of an image and secondly, we need a robust method which can
discriminate these features for making them belonging to different clusters
such that intra-class variance is less and inter-class variance is high. Often
these two aspects are dealt with independently and thus the features are not
sufficient enough to partition the data meaningfully. In this paper, we propose
a method where we discover these features required for the separation of the
images using deep autoencoder. Our method learns the image representation
features automatically for the purpose of clustering and also select a coherent
image and an incoherent image simultaneously for a given image so that the
feature representation learning can learn better discriminative features for
grouping the similar images in a cluster and at the same time separating the
dissimilar images across clusters. Experiment results show that our method
produces significantly better result than the state-of-the-art methods and we
also show that our method is more generalized across different dataset without
using any pre-trained model like other existing methods.Comment: Published in WACV, 201
Local Aggregation for Unsupervised Learning of Visual Embeddings
Unsupervised approaches to learning in neural networks are of substantial
interest for furthering artificial intelligence, both because they would enable
the training of networks without the need for large numbers of expensive
annotations, and because they would be better models of the kind of
general-purpose learning deployed by humans. However, unsupervised networks
have long lagged behind the performance of their supervised counterparts,
especially in the domain of large-scale visual recognition. Recent developments
in training deep convolutional embeddings to maximize non-parametric instance
separation and clustering objectives have shown promise in closing this gap.
Here, we describe a method that trains an embedding function to maximize a
metric of local aggregation, causing similar data instances to move together in
the embedding space, while allowing dissimilar instances to separate. This
aggregation metric is dynamic, allowing soft clusters of different scales to
emerge. We evaluate our procedure on several large-scale visual recognition
datasets, achieving state-of-the-art unsupervised transfer learning performance
on object recognition in ImageNet, scene recognition in Places 205, and object
detection in PASCAL VOC
CNN-Based Deep Architecture for Reinforced Concrete Delamination Segmentation Through Thermography
Delamination assessment of the bridge deck plays a vital role for bridge
health monitoring. Thermography as one of the nondestructive technologies for
delamination detection has the advantage of efficient data acquisition. But
there are challenges on the interpretation of data for accurate delamination
shape profiling. Due to the environmental variation and the irregular presence
of delamination size and depth, conventional processing methods based on
temperature contrast fall short in accurate segmentation of delamination.
Inspired by the recent development of deep learning architecture for image
segmentation, the Convolutional Neural Network (CNN) based framework was
investigated for the applicability of delamination segmentation under
variations in temperature contrast and shape diffusion. The models were
developed based on Dense Convolutional Network (DenseNet) and trained on
thermal images collected for mimicked delamination in concrete slabs with
different depths under experimental setup. The results suggested satisfactory
performance of accurate profiling the delamination shapes.Comment: Accepted for the 2019 ASCE International Conference on Computing in
Civil Engineerin
Successive Embedding and Classification Loss for Aerial Image Classification
Deep neural networks can be effective means to automatically classify aerial
images but is easy to overfit to the training data. It is critical for trained
neural networks to be robust to variations that exist between training and test
environments. To address the overfitting problem in aerial image
classification, we consider the neural network as successive transformations of
an input image into embedded feature representations and ultimately into a
semantic class label, and train neural networks to optimize image
representations in the embedded space in addition to optimizing the final
classification score. We demonstrate that networks trained with this dual
embedding and classification loss outperform networks with classification loss
only. %We also study placing the embedding loss on different network layers. We
also find that moving the embedding loss from commonly-used feature space to
the classifier space, which is the space just before softmax nonlinearization,
leads to the best classification performance for aerial images. Visualizations
of the network's embedded representations reveal that the embedding loss
encourages greater separation between target class clusters for both training
and testing partitions of two aerial image classification benchmark datasets,
MSTAR and AID. Our code is publicly available on GitHub
Deep metric learning using Triplet network
Deep learning has proven itself as a successful set of models for learning
useful semantic representations of data. These, however, are mostly implicitly
learned as part of a classification task. In this paper we propose the triplet
network model, which aims to learn useful representations by distance
comparisons. A similar model was defined by Wang et al. (2014), tailor made for
learning a ranking for image information retrieval. Here we demonstrate using
various datasets that our model learns a better representation than that of its
immediate competitor, the Siamese network. We also discuss future possible
usage as a framework for unsupervised learning
Deep Randomized Ensembles for Metric Learning
Learning embedding functions, which map semantically related inputs to nearby
locations in a feature space supports a variety of classification and
information retrieval tasks. In this work, we propose a novel, generalizable
and fast method to define a family of embedding functions that can be used as
an ensemble to give improved results. Each embedding function is learned by
randomly bagging the training labels into small subsets. We show experimentally
that these embedding ensembles create effective embedding functions. The
ensemble output defines a metric space that improves state of the art
performance for image retrieval on CUB-200-2011, Cars-196, In-Shop Clothes
Retrieval and VehicleID.Comment: ECCV 201
Canonical and Compact Point Cloud Representation for Shape Classification
We present a novel compact point cloud representation that is inherently
invariant to scale, coordinate change and point permutation. The key idea is to
parametrize a distance field around an individual shape into a unique,
canonical, and compact vector in an unsupervised manner. We firstly project a
distance field to a D canonical space using singular value decomposition. We
then train a neural network for each instance to non-linearly embed its
distance field into network parameters. We employ a bias-free Extreme Learning
Machine (ELM) with ReLU activation units, which has scale-factor commutative
property between layers. We demonstrate the descriptiveness of the
instance-wise, shape-embedded network parameters by using them to classify
shapes in D datasets. Our learning-based representation requires minimal
augmentation and simple neural networks, where previous approaches demand
numerous representations to handle coordinate change and point permutation.Comment: 16 pages, 5 figure
Histopathologic Image Processing: A Review
Histopathologic Images (HI) are the gold standard for evaluation of some
tumors. However, the analysis of such images is challenging even for
experienced pathologists, resulting in problems of inter and intra observer.
Besides that, the analysis is time and resource consuming. One of the ways to
accelerate such an analysis is by using Computer Aided Diagnosis systems. In
this work we present a literature review about the computing techniques to
process HI, including shallow and deep methods. We cover the most common tasks
for processing HI such as segmentation, feature extraction, unsupervised
learning and supervised learning. A dataset section show some datasets found
during the literature review. We also bring a study case of breast cancer
classification using a mix of deep and shallow machine learning methods. The
proposed method obtained an accuracy of 91% in the best case, outperforming the
compared baseline of the dataset
- …