1,803 research outputs found
Improved Techniques for Adversarial Discriminative Domain Adaptation
Adversarial discriminative domain adaptation (ADDA) is an efficient framework
for unsupervised domain adaptation in image classification, where the source
and target domains are assumed to have the same classes, but no labels are
available for the target domain. We investigate whether we can improve
performance of ADDA with a new framework and new loss formulations. Following
the framework of semi-supervised GANs, we first extend the discriminator output
over the source classes, in order to model the joint distribution over domain
and task. We thus leverage on the distribution over the source encoder
posteriors (which is fixed during adversarial training) and propose maximum
mean discrepancy (MMD) and reconstruction-based loss functions for aligning the
target encoder distribution to the source domain. We compare and provide a
comprehensive analysis of how our framework and loss formulations extend over
simple multi-class extensions of ADDA and other discriminative variants of
semi-supervised GANs. In addition, we introduce various forms of regularization
for stabilizing training, including treating the discriminator as a denoising
autoencoder and regularizing the target encoder with source examples to reduce
overfitting under a contraction mapping (i.e., when the target per-class
distributions are contracting during alignment with the source). Finally, we
validate our framework on standard domain adaptation datasets, such as SVHN and
MNIST. We also examine how our framework benefits recognition problems based on
modalities that lack training data, by introducing and evaluating on a
neuromorphic vision sensing (NVS) sign language recognition dataset, where the
source and target domains constitute emulated and real neuromorphic spike
events respectively. Our results on all datasets show that our proposal
competes or outperforms the state-of-the-art in unsupervised domain adaptation.Comment: To appear in IEEE Transactions on Image Processin
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising
In many real-world classification problems, the labels of training examples
are randomly corrupted. Most previous theoretical work on classification with
label noise assumes that the two classes are separable, that the label noise is
independent of the true class label, or that the noise proportions for each
class are known. In this work, we give conditions that are necessary and
sufficient for the true class-conditional distributions to be identifiable.
These conditions are weaker than those analyzed previously, and allow for the
classes to be nonseparable and the noise levels to be asymmetric and unknown.
The conditions essentially state that a majority of the observed labels are
correct and that the true class-conditional distributions are "mutually
irreducible," a concept we introduce that limits the similarity of the two
distributions. For any label noise problem, there is a unique pair of true
class-conditional distributions satisfying the proposed conditions, and we
argue that this pair corresponds in a certain sense to maximal denoising of the
observed distributions.
Our results are facilitated by a connection to "mixture proportion
estimation," which is the problem of estimating the maximal proportion of one
distribution that is present in another. We establish a novel rate of
convergence result for mixture proportion estimation, and apply this to obtain
consistency of a discrimination rule based on surrogate loss minimization.
Experimental results on benchmark data and a nuclear particle classification
problem demonstrate the efficacy of our approach
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
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