312 research outputs found
Generative Adversarial Active Learning
We propose a new active learning by query synthesis approach using Generative
Adversarial Networks (GAN). Different from regular active learning, the
resulting algorithm adaptively synthesizes training instances for querying to
increase learning speed. We generate queries according to the uncertainty
principle, but our idea can work with other active learning principles. We
report results from various numerical experiments to demonstrate the
effectiveness the proposed approach. In some settings, the proposed algorithm
outperforms traditional pool-based approaches. To the best our knowledge, this
is the first active learning work using GAN
Variational Adversarial Active Learning
Active learning aims to develop label-efficient algorithms by sampling the
most representative queries to be labeled by an oracle. We describe a
pool-based semi-supervised active learning algorithm that implicitly learns
this sampling mechanism in an adversarial manner. Unlike conventional active
learning algorithms, our approach is task agnostic, i.e., it does not depend on
the performance of the task for which we are trying to acquire labeled data.
Our method learns a latent space using a variational autoencoder (VAE) and an
adversarial network trained to discriminate between unlabeled and labeled data.
The mini-max game between the VAE and the adversarial network is played such
that while the VAE tries to trick the adversarial network into predicting that
all data points are from the labeled pool, the adversarial network learns how
to discriminate between dissimilarities in the latent space. We extensively
evaluate our method on various image classification and semantic segmentation
benchmark datasets and establish a new state of the art on
, , ,
, and . Our results demonstrate that our
adversarial approach learns an effective low dimensional latent space in
large-scale settings and provides for a computationally efficient sampling
method. Our code is available at https://github.com/sinhasam/vaal.Comment: First two authors contributed equally, listed alphabetically.
Accepted as Oral at ICCV 201
State-Relabeling Adversarial Active Learning
Active learning is to design label-efficient algorithms by sampling the most
representative samples to be labeled by an oracle. In this paper, we propose a
state relabeling adversarial active learning model (SRAAL), that leverages both
the annotation and the labeled/unlabeled state information for deriving the
most informative unlabeled samples. The SRAAL consists of a representation
generator and a state discriminator. The generator uses the complementary
annotation information with traditional reconstruction information to generate
the unified representation of samples, which embeds the semantic into the whole
data representation. Then, we design an online uncertainty indicator in the
discriminator, which endues unlabeled samples with different importance. As a
result, we can select the most informative samples based on the discriminator's
predicted state. We also design an algorithm to initialize the labeled pool,
which makes subsequent sampling more efficient. The experiments conducted on
various datasets show that our model outperforms the previous state-of-art
active learning methods and our initially sampling algorithm achieves better
performance.Comment: Accepted as Oral at CVPR 202
Adversarial Learning: A Critical Review and Active Learning Study
This papers consists of two parts. The first is a critical review of prior
art on adversarial learning, identifying some significant limitations of
previous works. The second part is an experimental study considering
adversarial active learning and an investigation of the efficacy of a mixed
sample selection strategy for combating an adversary who attempts to disrupt
the classifier learning
SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs
Active learning (AL) on attributed graphs has received increasing attention
with the prevalence of graph-structured data. Although AL has been widely
studied for alleviating label sparsity issues with the conventional non-related
data, how to make it effective over attributed graphs remains an open research
question. Existing AL algorithms on graphs attempt to reuse the classic AL
query strategies designed for non-related data. However, they suffer from two
major limitations. First, different AL query strategies calculated in distinct
scoring spaces are often naively combined to determine which nodes to be
labelled. Second, the AL query engine and the learning of the classifier are
treated as two separating processes, resulting in unsatisfactory performance.
In this paper, we propose a SEmi-supervised Adversarial active Learning (SEAL)
framework on attributed graphs, which fully leverages the representation power
of deep neural networks and devises a novel AL query strategy in an adversarial
way. Our framework learns two adversarial components: a graph embedding network
that encodes both the unlabelled and labelled nodes into a latent space,
expecting to trick the discriminator to regard all nodes as already labelled,
and a semi-supervised discriminator network that distinguishes the unlabelled
from the existing labelled nodes in the latent space. The divergence score,
generated by the discriminator in a unified latent space, serves as the
informativeness measure to actively select the most informative node to be
labelled by an oracle. The two adversarial components form a closed loop to
mutually and simultaneously reinforce each other towards enhancing the active
learning performance. Extensive experiments on four real-world networks
validate the effectiveness of the SEAL framework with superior performance
improvements to state-of-the-art baselines
Adversarial Active Learning for Deep Networks: a Margin Based Approach
We propose a new active learning strategy designed for deep neural networks.
The goal is to minimize the number of data annotation queried from an oracle
during training. Previous active learning strategies scalable for deep networks
were mostly based on uncertain sample selection. In this work, we focus on
examples lying close to the decision boundary. Based on theoretical works on
margin theory for active learning, we know that such examples may help to
considerably decrease the number of annotations. While measuring the exact
distance to the decision boundaries is intractable, we propose to rely on
adversarial examples. We do not consider anymore them as a threat instead we
exploit the information they provide on the distribution of the input space in
order to approximate the distance to decision boundaries. We demonstrate
empirically that adversarial active queries yield faster convergence of CNNs
trained on MNIST, the Shoe-Bag and the Quick-Draw datasets
Transformation Based Deep Anomaly Detection in Astronomical Images
In this work, we propose several enhancements to a geometric transformation
based model for anomaly detection in images (GeoTranform). The model assumes
that the anomaly class is unknown and that only inlier samples are available
for training. We introduce new filter based transformations useful for
detecting anomalies in astronomical images, that highlight artifact properties
to make them more easily distinguishable from real objects. In addition, we
propose a transformation selection strategy that allows us to find
indistinguishable pairs of transformations. This results in an improvement of
the area under the Receiver Operating Characteristic curve (AUROC) and accuracy
performance, as well as in a dimensionality reduction. The models were tested
on astronomical images from the High Cadence Transient Survey (HiTS) and Zwicky
Transient Facility (ZTF) datasets. The best models obtained an average AUROC of
99.20% for HiTS and 91.39% for ZTF. The improvement over the original
GeoTransform algorithm and baseline methods such as One-Class Support Vector
Machine, and deep learning based methods is significant both statistically and
in practice.Comment: 8 pages, 6 figures, 4 tables. Accepted for publication in proceedings
of the IEEE World Congress on Computational Intelligence (IEEE WCCI),
Glasgow, UK, 19-24 July, 202
Learning to Make Analogies by Contrasting Abstract Relational Structure
Analogical reasoning has been a principal focus of various waves of AI
research. Analogy is particularly challenging for machines because it requires
relational structures to be represented such that they can be flexibly applied
across diverse domains of experience. Here, we study how analogical reasoning
can be induced in neural networks that learn to perceive and reason about raw
visual data. We find that the critical factor for inducing such a capacity is
not an elaborate architecture, but rather, careful attention to the choice of
data and the manner in which it is presented to the model. The most robust
capacity for analogical reasoning is induced when networks learn analogies by
contrasting abstract relational structures in their input domains, a training
method that uses only the input data to force models to learn about important
abstract features. Using this technique we demonstrate capacities for complex,
visual and symbolic analogy making and generalisation in even the simplest
neural network architectures
Dual Active Sampling on Batch-Incremental Active Learning
Recently, Convolutional Neural Networks (CNNs) have shown unprecedented
success in the field of computer vision, especially on challenging image
classification tasks by relying on a universal approach, i.e., training a deep
model on a massive dataset of supervised examples. While unlabeled data are
often an abundant resource, collecting a large set of labeled data, on the
other hand, are very expensive, which often require considerable human efforts.
One way to ease out this is to effectively select and label highly informative
instances from a pool of unlabeled data (i.e., active learning). This paper
proposed a new method of batch-mode active learning, Dual Active Sampling(DAS),
which is based on a simple assumption, if two deep neural networks (DNNs) of
the same structure and trained on the same dataset give significantly different
output for a given sample, then that particular sample should be picked for
additional training. While other state of the art methods in this field usually
require intensive computational power or relying on a complicated structure,
DAS is simpler to implement and, managed to get improved results on Cifar-10
with preferable computational time compared to the core-set method.Comment: 6 page
Knowledge Modelling and Active Learning in Manufacturing
The increasing digitalization of the manufacturing domain requires adequate
knowledge modeling to capture relevant information. Ontologies and Knowledge
Graphs provide means to model and relate a wide range of concepts, problems,
and configurations. Both can be used to generate new knowledge through
deductive inference and identify missing knowledge. While digitalization
increases the amount of data available, much data is not labeled and cannot be
directly used to train supervised machine learning models. Active learning can
be used to identify the most informative data instances for which to obtain
users' feedback, reduce friction, and maximize knowledge acquisition. By
combining semantic technologies and active learning, multiple use cases in the
manufacturing domain can be addressed taking advantage of the available
knowledge and data
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