2,066 research outputs found
HyperVAE: A Minimum Description Length Variational Hyper-Encoding Network
We propose a framework called HyperVAE for encoding distributions of
distributions. When a target distribution is modeled by a VAE, its neural
network parameters \theta is drawn from a distribution p(\theta) which is
modeled by a hyper-level VAE. We propose a variational inference using Gaussian
mixture models to implicitly encode the parameters \theta into a low
dimensional Gaussian distribution. Given a target distribution, we predict the
posterior distribution of the latent code, then use a matrix-network decoder to
generate a posterior distribution q(\theta). HyperVAE can encode the parameters
\theta in full in contrast to common hyper-networks practices, which generate
only the scale and bias vectors as target-network parameters. Thus HyperVAE
preserves much more information about the model for each task in the latent
space. We discuss HyperVAE using the minimum description length (MDL) principle
and show that it helps HyperVAE to generalize. We evaluate HyperVAE in density
estimation tasks, outlier detection and discovery of novel design classes,
demonstrating its efficacy
Learning Representations for Novelty and Anomaly Detection
The problem of novelty or anomaly detection refers to the ability to automatically
identify data samples that differ from a notion of normality. Techniques
that address this problem are necessary in many applications, like in medical
diagnosis, autonomous driving, fraud detection, or cyber-attack detection, just to
mention a few. The problem is inherently challenging because of the openness of
the space of distributions that characterize novelty or outlier data points. This is
often matched with the inability to adequately represent such distributions due
to the lack of representative data.
In this dissertation we address the challenge above by making several contributions.
(a)We introduce an unsupervised framework for novelty detection,
which is based on deep learning techniques, and which does not require labeled
data representing the distribution of outliers. (b) The framework is general and
based on first principles by detecting anomalies via computing their probabilities
according to the distribution representing normality. (c) The framework can
handle high-dimensional data such as images, by performing a non-linear dimensionality
reduction of the input space into an isometric lower-dimensional space,
leading to a computationally efficient method. (d) The framework is guarded
from the potential inclusion of distributions of outliers into the distribution of
normality by favoring that only inlier data can be well represented by the model.
(e) The methods are evaluated extensively on multiple computer vision benchmark
datasets, where it is shown that they compare favorably with the state of
the art
Representation Learning with Adversarial Latent Autoencoders
A large number of deep learning methods applied to computer vision problems require encoder-decoder maps. These methods include, but are not limited to, self-representation learning, generalization, few-shot learning, and novelty detection. Encoder-decoder maps are also useful for photo manipulation, photo editing, superresolution, etc. Encoder-decoder maps are typically learned using autoencoder networks.Traditionally, autoencoder reciprocity is achieved in the image-space using pixel-wisesimilarity loss, which has a widely known flaw of producing non-realistic reconstructions. This flaw is typical for the Variational Autoencoder (VAE) family and is not only limited to pixel-wise similarity losses, but is common to all methods relying upon the explicit maximum likelihood training paradigm, as opposed to an implicit one. Likelihood maximization, coupled with poor decoder distribution leads to poor or blurry reconstructions at best. Generative Adversarial Networks (GANs) on the other hand, perform an implicit maximization of the likelihood by solving a minimax game, thus bypassing the issues derived from the explicit maximization. This provides GAN architectures with remarkable generative power, enabling the generation of high-resolution images of humans, which are indistinguishable from real photos to the naked eye. However, GAN architectures lack inference capabilities, which makes them unsuitable for training encoder-decoder maps, effectively limiting their application space.We introduce an autoencoder architecture that (a) is free from the consequences ofmaximizing the likelihood directly, (b) produces reconstructions competitive in quality with state-of-the-art GAN architectures, and (c) allows learning disentangled representations, which makes it useful in a variety of problems. We show that the proposed architecture and training paradigm significantly improves the state-of-the-art in novelty and anomaly detection methods, it enables novel kinds of image manipulations, and has significant potential for other applications
AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling
Anomaly detection or more generally outliers detection is one of the most
popular and challenging subject in theoretical and applied machine learning.
The main challenge is that in general we have access to very few labeled data
or no labels at all. In this paper, we present a new semi-supervised anomaly
detection method called \textbf{AnoRand} by combining a deep learning
architecture with random synthetic label generation. The proposed architecture
has two building blocks: (1) a noise detection (ND) block composed of feed
forward ferceptron and (2) an autoencoder (AE) block. The main idea of this new
architecture is to learn one class (e.g. the majority class in case of anomaly
detection) as well as possible by taking advantage of the ability of auto
encoders to represent data in a latent space and the ability of Feed Forward
Perceptron (FFP) to learn one class when the data is highly imbalanced. First,
we create synthetic anomalies by randomly disturbing (add noise) few samples
(e.g. 2\%) from the training set. Second, we use the normal and the synthetic
samples as input to our model. We compared the performance of the proposed
method to 17 state-of-the-art unsupervised anomaly detection method on
synthetic datasets and 57 real-world datasets. Our results show that this new
method generally outperforms most of the state-of-the-art methods and has the
best performance (AUC ROC and AUC PR) on the vast majority of reference
datasets. We also tested our method in a supervised way by using the actual
labels to train the model. The results show that it has very good performance
compared to most of state-of-the-art supervised algorithms
- …