9,693 research outputs found
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
With widespread applications of artificial intelligence (AI), the
capabilities of the perception, understanding, decision-making and control for
autonomous systems have improved significantly in the past years. When
autonomous systems consider the performance of accuracy and transferability,
several AI methods, like adversarial learning, reinforcement learning (RL) and
meta-learning, show their powerful performance. Here, we review the
learning-based approaches in autonomous systems from the perspectives of
accuracy and transferability. Accuracy means that a well-trained model shows
good results during the testing phase, in which the testing set shares a same
task or a data distribution with the training set. Transferability means that
when a well-trained model is transferred to other testing domains, the accuracy
is still good. Firstly, we introduce some basic concepts of transfer learning
and then present some preliminaries of adversarial learning, RL and
meta-learning. Secondly, we focus on reviewing the accuracy or transferability
or both of them to show the advantages of adversarial learning, like generative
adversarial networks (GANs), in typical computer vision tasks in autonomous
systems, including image style transfer, image superresolution, image
deblurring/dehazing/rain removal, semantic segmentation, depth estimation,
pedestrian detection and person re-identification (re-ID). Then, we further
review the performance of RL and meta-learning from the aspects of accuracy or
transferability or both of them in autonomous systems, involving pedestrian
tracking, robot navigation and robotic manipulation. Finally, we discuss
several challenges and future topics for using adversarial learning, RL and
meta-learning in autonomous systems
Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving
Generative Adversarial Networks (GAN) have gained a lot of popularity from
their introduction in 2014 till present. Research on GAN is rapidly growing and
there are many variants of the original GAN focusing on various aspects of deep
learning. GAN are perceived as the most impactful direction of machine learning
in the last decade. This paper focuses on the application of GAN in autonomous
driving including topics such as advanced data augmentation, loss function
learning, semi-supervised learning, etc. We formalize and review key
applications of adversarial techniques and discuss challenges and open problems
to be addressed.Comment: Accepted for publication in Electronic Imaging, Autonomous Vehicles
and Machines 2019. arXiv admin note: text overlap with arXiv:1606.05908 by
other author
Adversarial Gain
Adversarial examples can be defined as inputs to a model which induce a
mistake - where the model output is different than that of an oracle, perhaps
in surprising or malicious ways. Original models of adversarial attacks are
primarily studied in the context of classification and computer vision tasks.
While several attacks have been proposed in natural language processing (NLP)
settings, they often vary in defining the parameters of an attack and what a
successful attack would look like. The goal of this work is to propose a
unifying model of adversarial examples suitable for NLP tasks in both
generative and classification settings. We define the notion of adversarial
gain: based in control theory, it is a measure of the change in the output of a
system relative to the perturbation of the input (caused by the so-called
adversary) presented to the learner. This definition, as we show, can be used
under different feature spaces and distance conditions to determine attack or
defense effectiveness across different intuitive manifolds. This notion of
adversarial gain not only provides a useful way for evaluating adversaries and
defenses, but can act as a building block for future work in robustness under
adversaries due to its rooted nature in stability and manifold theory
Data Augmentation Generative Adversarial Networks
Effective training of neural networks requires much data. In the low-data
regime, parameters are underdetermined, and learnt networks generalise poorly.
Data Augmentation alleviates this by using existing data more effectively.
However standard data augmentation produces only limited plausible alternative
data. Given there is potential to generate a much broader set of augmentations,
we design and train a generative model to do data augmentation. The model,
based on image conditional Generative Adversarial Networks, takes data from a
source domain and learns to take any data item and generalise it to generate
other within-class data items. As this generative process does not depend on
the classes themselves, it can be applied to novel unseen classes of data. We
show that a Data Augmentation Generative Adversarial Network (DAGAN) augments
standard vanilla classifiers well. We also show a DAGAN can enhance few-shot
learning systems such as Matching Networks. We demonstrate these approaches on
Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and VGG-Face data. In
our experiments we can see over 13% increase in accuracy in the low-data regime
experiments in Omniglot (from 69% to 82%), EMNIST (73.9% to 76%) and VGG-Face
(4.5% to 12%); in Matching Networks for Omniglot we observe an increase of 0.5%
(from 96.9% to 97.4%) and an increase of 1.8% in EMNIST (from 59.5% to 61.3%).Comment: 10 page
Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation
Text generation with generative adversarial networks (GANs) can be divided
into the text-based and code-based categories according to the type of signals
used for discrimination. In this work, we introduce a novel text-based approach
called Soft-GAN to effectively exploit GAN setup for text generation. We
demonstrate how autoencoders (AEs) can be used for providing a continuous
representation of sentences, which we will refer to as soft-text. This soft
representation will be used in GAN discrimination to synthesize similar
soft-texts. We also propose hybrid latent code and text-based GAN (LATEXT-GAN)
approaches with one or more discriminators, in which a combination of the
latent code and the soft-text is used for GAN discriminations. We perform a
number of subjective and objective experiments on two well-known datasets (SNLI
and Image COCO) to validate our techniques. We discuss the results using
several evaluation metrics and show that the proposed techniques outperform the
traditional GAN-based text-generation methods
Self-training with Noisy Student improves ImageNet classification
We present Noisy Student Training, a semi-supervised learning approach that
works well even when labeled data is abundant. Noisy Student Training achieves
88.4% top-1 accuracy on ImageNet, which is 2.0% better than the
state-of-the-art model that requires 3.5B weakly labeled Instagram images. On
robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to
83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces
ImageNet-P mean flip rate from 27.8 to 12.2.
Noisy Student Training extends the idea of self-training and distillation
with the use of equal-or-larger student models and noise added to the student
during learning. On ImageNet, we first train an EfficientNet model on labeled
images and use it as a teacher to generate pseudo labels for 300M unlabeled
images. We then train a larger EfficientNet as a student model on the
combination of labeled and pseudo labeled images. We iterate this process by
putting back the student as the teacher. During the learning of the student, we
inject noise such as dropout, stochastic depth, and data augmentation via
RandAugment to the student so that the student generalizes better than the
teacher. Models are available at
https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet.
Code is available at https://github.com/google-research/noisystudent.Comment: CVPR 202
Towards an Understanding of Neural Networks in Natural-Image Spaces
Two major uncertainties, dataset bias and adversarial examples, prevail in
state-of-the-art AI algorithms with deep neural networks. In this paper, we
present an intuitive explanation for these issues as well as an interpretation
of the performance of deep networks in a natural-image space. The explanation
consists of two parts: the philosophy of neural networks and a hypothetical
model of natural-image spaces. Following the explanation, we 1) demonstrate
that the values of training samples differ, 2) provide incremental boost to the
accuracy of a CIFAR-10 classifier by introducing an additional "random-noise"
category during training, 3) alleviate over-fitting thereby enhancing the
robustness against adversarial examples by detecting and excluding illusive
training samples that are consistently misclassified. Our overall contribution
is therefore twofold. First, while most existing algorithms treat data equally
and have a strong appetite for more data, we demonstrate in contrast that an
individual datum can sometimes have disproportionate and counterproductive
influence and that it is not always better to train neural networks with more
data. Next, we consider more thoughtful strategies by taking into account the
geometric and topological properties of natural-image spaces to which deep
networks are applied
Transfer Adaptation Learning: A Decade Survey
The world we see is ever-changing and it always changes with people, things,
and the environment. Domain is referred to as the state of the world at a
certain moment. A research problem is characterized as transfer adaptation
learning (TAL) when it needs knowledge correspondence between different
moments/domains. Conventional machine learning aims to find a model with the
minimum expected risk on test data by minimizing the regularized empirical risk
on the training data, which, however, supposes that the training and test data
share similar joint probability distribution. TAL aims to build models that can
perform tasks of target domain by learning knowledge from a semantic related
but distribution different source domain. It is an energetic research filed of
increasing influence and importance, which is presenting a blowout publication
trend. This paper surveys the advances of TAL methodologies in the past decade,
and the technical challenges and essential problems of TAL have been observed
and discussed with deep insights and new perspectives. Broader solutions of
transfer adaptation learning being created by researchers are identified, i.e.,
instance re-weighting adaptation, feature adaptation, classifier adaptation,
deep network adaptation and adversarial adaptation, which are beyond the early
semi-supervised and unsupervised split. The survey helps researchers rapidly
but comprehensively understand and identify the research foundation, research
status, theoretical limitations, future challenges and under-studied issues
(universality, interpretability, and credibility) to be broken in the field
toward universal representation and safe applications in open-world scenarios.Comment: 26 pages, 4 figure
An Adversarial Approach to High-Quality, Sentiment-Controlled Neural Dialogue Generation
In this work, we propose a method for neural dialogue response generation
that allows not only generating semantically reasonable responses according to
the dialogue history, but also explicitly controlling the sentiment of the
response via sentiment labels. Our proposed model is based on the paradigm of
conditional adversarial learning; the training of a sentiment-controlled
dialogue generator is assisted by an adversarial discriminator which assesses
the fluency and feasibility of the response generating from the dialogue
history and a given sentiment label. Because of the flexibility of our
framework, the generator could be a standard sequence-to-sequence (SEQ2SEQ)
model or a more complicated one such as a conditional variational
autoencoder-based SEQ2SEQ model. Experimental results using automatic and human
evaluation both demonstrate that our proposed framework is able to generate
both semantically reasonable and sentiment-controlled dialogue responses.Comment: DEEP-DIAL 201
Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition
We investigate the effectiveness of generative adversarial networks (GANs)
for speech enhancement, in the context of improving noise robustness of
automatic speech recognition (ASR) systems. Prior work demonstrates that GANs
can effectively suppress additive noise in raw waveform speech signals,
improving perceptual quality metrics; however this technique was not justified
in the context of ASR. In this work, we conduct a detailed study to measure the
effectiveness of GANs in enhancing speech contaminated by both additive and
reverberant noise. Motivated by recent advances in image processing, we propose
operating GANs on log-Mel filterbank spectra instead of waveforms, which
requires less computation and is more robust to reverberant noise. While GAN
enhancement improves the performance of a clean-trained ASR system on noisy
speech, it falls short of the performance achieved by conventional multi-style
training (MTR). By appending the GAN-enhanced features to the noisy inputs and
retraining, we achieve a 7% WER improvement relative to the MTR system.Comment: Published as a conference paper at ICASSP 201
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