551 research outputs found
Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation
Unlike unsupervised approaches such as autoencoders that learn to reconstruct
their inputs, this paper introduces an alternative approach to unsupervised
feature learning called divergent discriminative feature accumulation (DDFA)
that instead continually accumulates features that make novel discriminations
among the training set. Thus DDFA features are inherently discriminative from
the start even though they are trained without knowledge of the ultimate
classification problem. Interestingly, DDFA also continues to add new features
indefinitely (so it does not depend on a hidden layer size), is not based on
minimizing error, and is inherently divergent instead of convergent, thereby
providing a unique direction of research for unsupervised feature learning. In
this paper the quality of its learned features is demonstrated on the MNIST
dataset, where its performance confirms that indeed DDFA is a viable technique
for learning useful features.Comment: Corrected citation formattin
Training Neural Networks Through the Integration of Evolution and Gradient Descent
Neural networks have achieved widespread adoption due to both their applicability to a wide range of problems and their success relative to other machine learning algorithms. The training of neural networks is achieved through any of several paradigms, most prominently gradient-based approaches (including deep learning), but also through up-and-coming approaches like neuroevolution. However, while both of these neural network training paradigms have seen major improvements over the past decade, little work has been invested in developing algorithms that incorporate the advances from both deep learning and neuroevolution. This dissertation introduces two new algorithms that are steps towards the integration of gradient descent and neuroevolution for training neural networks. The first is (1) the Limited Evaluation Evolutionary Algorithm (LEEA), which implements a novel form of evolution where individuals are partially evaluated, allowing rapid learning and enabling the evolutionary algorithm to behave more like gradient descent. This conception provides a critical stepping stone to future algorithms that more tightly couple evolutionary and gradient descent components. The second major algorithm (2) is Divergent Discriminative Feature Accumulation (DDFA), which combines a neuroevolution phase, where features are collected in an unsupervised manner, with a gradient descent phase for fine tuning of the neural network weights. The neuroevolution phase of DDFA utilizes an indirect encoding and novelty search, which are sophisticated neuroevolution components rarely incorporated into gradient descent-based systems. Further contributions of this work that build on DDFA include (3) an empirical analysis to identify an effective distance function for novelty search in high dimensions and (4) the extension of DDFA for the purpose of discovering convolutional features. The results of these DDFA experiments together show that DDFA discovers features that are effective as a starting point for gradient descent, with significant improvement over gradient descent alone. Additionally, the method of collecting features in an unsupervised manner allows DDFA to be applied to domains with abundant unlabeled data and relatively sparse labeled data. This ability is highlighted in the STL-10 domain, where DDFA is shown to make effective use of unlabeled data
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
Machine learning for outlier detection in medical imaging
Outlier detection is an important problem with diverse practical applications. In medical imaging, there are many diagnostic tasks that can be framed as outlier detection. Since pathologies can manifest in so many different ways, the goal is typically to learn from normal, healthy data and identify any deviations. Unfortunately, many outliers in the medical domain can be subtle and specific, making them difficult to detect without labelled examples. This thesis analyzes some of the nuances of medical data and the value of labels in this context. It goes on to propose several strategies for unsupervised learning. More specifically, these methods are designed to learn discriminative features from data of a single class. One approach uses divergent search to continually find different ways to partition the data and thereby accumulates a repertoire of features. The other proposed methods are based on a self-supervised task that distorts normal data to form a contrasting class. A network can then be trained to localize the irregularities and estimate the degree of foreign interference. This basic technique is further enhanced using advanced image editing to create more natural irregularities. Lastly, the same self-supervised task is repurposed for few-shot learning to create a framework for adaptive outlier detection. These proposed methods are able to outperform conventional strategies across a range of datasets including brain MRI, abdominal CT, chest X-ray, and fetal ultrasound data. In particular, these methods excel at detecting more subtle irregularities. This complements existing methods and aims to maximize benefit to clinicians by detecting fine-grained anomalies that can otherwise require intense scrutiny. Note that all approaches to outlier detection must accept some assumptions; these will affect which types of outliers can be detected. As such, these methods aim for broad generalization within the most medically relevant categories. Ultimately, the hope is to support clinicians and to focus their attention and efforts on the data that warrants further analysis.Open Acces
What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification
Matching pedestrians across disjoint camera views, known as person
re-identification (re-id), is a challenging problem that is of importance to
visual recognition and surveillance. Most existing methods exploit local
regions within spatial manipulation to perform matching in local
correspondence. However, they essentially extract \emph{fixed} representations
from pre-divided regions for each image and perform matching based on the
extracted representation subsequently. For models in this pipeline, local finer
patterns that are crucial to distinguish positive pairs from negative ones
cannot be captured, and thus making them underperformed. In this paper, we
propose a novel deep multiplicative integration gating function, which answers
the question of \emph{what-and-where to match} for effective person re-id. To
address \emph{what} to match, our deep network emphasizes common local patterns
by learning joint representations in a multiplicative way. The network
comprises two Convolutional Neural Networks (CNNs) to extract convolutional
activations, and generates relevant descriptors for pedestrian matching. This
thus, leads to flexible representations for pair-wise images. To address
\emph{where} to match, we combat the spatial misalignment by performing
spatially recurrent pooling via a four-directional recurrent neural network to
impose spatial dependency over all positions with respect to the entire image.
The proposed network is designed to be end-to-end trainable to characterize
local pairwise feature interactions in a spatially aligned manner. To
demonstrate the superiority of our method, extensive experiments are conducted
over three benchmark data sets: VIPeR, CUHK03 and Market-1501.Comment: Published at Pattern Recognition, Elsevie
Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity
Most of the existing learning-based deraining methods are supervisedly
trained on synthetic rainy-clean pairs. The domain gap between the synthetic
and real rain makes them less generalized to complex real rainy scenes.
Moreover, the existing methods mainly utilize the property of the image or rain
layers independently, while few of them have considered their mutually
exclusive relationship. To solve above dilemma, we explore the intrinsic
intra-similarity within each layer and inter-exclusiveness between two layers
and propose an unsupervised non-local contrastive learning (NLCL) deraining
method. The non-local self-similarity image patches as the positives are
tightly pulled together, rain patches as the negatives are remarkably pushed
away, and vice versa. On one hand, the intrinsic self-similarity knowledge
within positive/negative samples of each layer benefits us to discover more
compact representation; on the other hand, the mutually exclusive property
between the two layers enriches the discriminative decomposition. Thus, the
internal self-similarity within each layer (similarity) and the external
exclusive relationship of the two layers (dissimilarity) serving as a generic
image prior jointly facilitate us to unsupervisedly differentiate the rain from
clean image. We further discover that the intrinsic dimension of the non-local
image patches is generally higher than that of the rain patches. This motivates
us to design an asymmetric contrastive loss to precisely model the compactness
discrepancy of the two layers for better discriminative decomposition. In
addition, considering that the existing real rain datasets are of low quality,
either small scale or downloaded from the internet, we collect a real
large-scale dataset under various rainy kinds of weather that contains
high-resolution rainy images.Comment: 16 pages, 15 figures. arXiv admin note: substantial text overlap with
arXiv:2203.1150
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