213 research outputs found
Self corrective Perturbations for Semantic Segmentation and Classification
Convolutional Neural Networks have been a subject of great importance over
the past decade and great strides have been made in their utility for producing
state of the art performance in many computer vision problems. However, the
behavior of deep networks is yet to be fully understood and is still an active
area of research. In this work, we present an intriguing behavior: pre-trained
CNNs can be made to improve their predictions by structurally perturbing the
input. We observe that these perturbations - referred as Guided Perturbations -
enable a trained network to improve its prediction performance without any
learning or change in network weights. We perform various ablative experiments
to understand how these perturbations affect the local context and feature
representations. Furthermore, we demonstrate that this idea can improve
performance of several existing approaches on semantic segmentation and scene
labeling tasks on the PASCAL VOC dataset and supervised classification tasks on
MNIST and CIFAR10 datasets.Comment: Accepted to ICCV 201
Regularizing deep networks using efficient layerwise adversarial training
Adversarial training has been shown to regularize deep neural networks in
addition to increasing their robustness to adversarial examples. However, its
impact on very deep state of the art networks has not been fully investigated.
In this paper, we present an efficient approach to perform adversarial training
by perturbing intermediate layer activations and study the use of such
perturbations as a regularizer during training. We use these perturbations to
train very deep models such as ResNets and show improvement in performance both
on adversarial and original test data. Our experiments highlight the benefits
of perturbing intermediate layer activations compared to perturbing only the
inputs. The results on CIFAR-10 and CIFAR-100 datasets show the merits of the
proposed adversarial training approach. Additional results on WideResNets show
that our approach provides significant improvement in classification accuracy
for a given base model, outperforming dropout and other base models of larger
size.Comment: Published at the Thirty-Second AAAI Conference on Artificial
Intelligence (AAAI-18). Official link:
https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/1663
Detecting Deep-Fake Videos from Appearance and Behavior
Synthetically-generated audios and videos -- so-called deep fakes -- continue
to capture the imagination of the computer-graphics and computer-vision
communities. At the same time, the democratization of access to technology that
can create sophisticated manipulated video of anybody saying anything continues
to be of concern because of its power to disrupt democratic elections, commit
small to large-scale fraud, fuel dis-information campaigns, and create
non-consensual pornography. We describe a biometric-based forensic technique
for detecting face-swap deep fakes. This technique combines a static biometric
based on facial recognition with a temporal, behavioral biometric based on
facial expressions and head movements, where the behavioral embedding is
learned using a CNN with a metric-learning objective function. We show the
efficacy of this approach across several large-scale video datasets, as well as
in-the-wild deep fakes
Unconstrained Facial Expression Transfer using Style-based Generator
Facial expression transfer and reenactment has been an important research
problem given its applications in face editing, image manipulation, and
fabricated videos generation. We present a novel method for image-based facial
expression transfer, leveraging the recent style-based GAN shown to be very
effective for creating realistic looking images. Given two face images, our
method can create plausible results that combine the appearance of one image
and the expression of the other. To achieve this, we first propose an
optimization procedure based on StyleGAN to infer hierarchical style vector
from an image that disentangle different attributes of the face. We further
introduce a linear combination scheme that fuses the style vectors of the two
given images and generate a new face that combines the expression and
appearance of the inputs. Our method can create high-quality synthesis with
accurate facial reenactment. Unlike many existing methods, we do not rely on
geometry annotations, and can be applied to unconstrained facial images of any
identities without the need for retraining, making it feasible to generate
large-scale expression-transferred results
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
Visual Domain Adaptation is a problem of immense importance in computer
vision. Previous approaches showcase the inability of even deep neural networks
to learn informative representations across domain shift. This problem is more
severe for tasks where acquiring hand labeled data is extremely hard and
tedious. In this work, we focus on adapting the representations learned by
segmentation networks across synthetic and real domains. Contrary to previous
approaches that use a simple adversarial objective or superpixel information to
aid the process, we propose an approach based on Generative Adversarial
Networks (GANs) that brings the embeddings closer in the learned feature space.
To showcase the generality and scalability of our approach, we show that we can
achieve state of the art results on two challenging scenarios of synthetic to
real domain adaptation. Additional exploratory experiments show that our
approach: (1) generalizes to unseen domains and (2) results in improved
alignment of source and target distributions.Comment: Accepted as spotlight talk at CVPR 2018. Code available here:
https://github.com/swamiviv/LSD-se
CULTURAL MANAGEMENT EDUCATION IN SOUTHEAST ASIA
Cultural Management could be defined as the process of planning, organizing, leading, and controlling material and nonmaterial culture to meet predetermined goals and objectives. Areas under the umbrella of Cultural Management include Arts Management, Museum Management, Cultural Heritage and Tourism, Cultural and Creative Industries, and Design Management. Over the last two decades, there has also been a growing number of academic programmes befitting the umbrella of Cultural Management, offered at undergraduate and postgraduate levels by institutions of higher education in Southeast Asia. This paper explores the current state of Cultural Management education in Southeast Asia, and thereafter, highlights possible synergies to align with ASEAN’s agenda. Several qualitative research methods were adopted, including content analysis, followed by thematic analysis, participant observations, and semi-structured interviews. This paper formally documents and discusses the Cultural Management curriculum of 10 Southeast Asian nations, using three key themes - top-down, bottom-up, and a combination of both top-down and bottom-up focus. Thereafter, the paper proposes two ways in which institutions of higher education in Southeast Asia could better synergize to meet the six strategies listed in the ASEAN Strategic Plan for Culture and Arts 2016-2025
One-Shot Domain Adaptation For Face Generation
In this paper, we propose a framework capable of generating face images that
fall into the same distribution as that of a given one-shot example. We
leverage a pre-trained StyleGAN model that already learned the generic face
distribution. Given the one-shot target, we develop an iterative optimization
scheme that rapidly adapts the weights of the model to shift the output's
high-level distribution to the target's. To generate images of the same
distribution, we introduce a style-mixing technique that transfers the
low-level statistics from the target to faces randomly generated with the
model. With that, we are able to generate an unlimited number of faces that
inherit from the distribution of both generic human faces and the one-shot
example. The newly generated faces can serve as augmented training data for
other downstream tasks. Such setting is appealing as it requires labeling very
few, or even one example, in the target domain, which is often the case of
real-world face manipulations that result from a variety of unknown and unique
distributions, each with extremely low prevalence. We show the effectiveness of
our one-shot approach for detecting face manipulations and compare it with
other few-shot domain adaptation methods qualitatively and quantitatively.Comment: Accepted to CVPR 202
Intelligent Conversational Bot for Massive Online Open Courses (MOOCs)
Massive Online Open Courses (MOOCs) which were introduced in 2008 has since
drawn attention around the world for both its advantages as well as criticism
on its drawbacks. One of the issues in MOOCs which is the lack of interactivity
with the instructor has brought conversational bot into the picture to fill in
this gap. In this study, a prototype of MOOCs conversational bot, MOOC-bot is
being developed and integrated into MOOCs website to respond to the learner
inquiries using text or speech input. MOOC-bot is using the popular Artificial
Intelligence Markup Language (AIML) to develop its knowledge base, leverage
from AIML capability to deliver appropriate responses and can be quickly
adapted to new knowledge domains. The system architecture of MOOC-bot consists
of knowledge base along with AIML interpreter, chat interface, MOOCs website
and Web Speech API to provide speech recognition and speech synthesis
capability. The initial MOOC-bot prototype has the general knowledge from the
past Loebner Prize winner - ALICE, frequent asked questions, and a content
offered by Universiti Teknikal Malaysia Melaka (UTeM). The evaluation of
MOOC-bot based on the past competition questions from Chatterbox Challenge
(CBC) and Loebner Prize has shown that it was able to provide correct answers
most of the time during the test and demonstrated the capability to prolong the
conversation. The advantages of MOOC-bot such as able to provide 24-hour
service that can serve different time zones, able to have knowledge in multiple
domains, and can be shared by multiple sites simultaneously have outweighed its
existing limitations
New Benchmarks for Learning on Non-Homophilous Graphs
Much data with graph structures satisfy the principle of homophily, meaning
that connected nodes tend to be similar with respect to a specific attribute.
As such, ubiquitous datasets for graph machine learning tasks have generally
been highly homophilous, rewarding methods that leverage homophily as an
inductive bias. Recent work has pointed out this particular focus, as new
non-homophilous datasets have been introduced and graph representation learning
models better suited for low-homophily settings have been developed. However,
these datasets are small and poorly suited to truly testing the effectiveness
of new methods in non-homophilous settings. We present a series of improved
graph datasets with node label relationships that do not satisfy the homophily
principle. Along with this, we introduce a new measure of the presence or
absence of homophily that is better suited than existing measures in different
regimes. We benchmark a range of simple methods and graph neural networks
across our proposed datasets, drawing new insights for further research. Data
and codes can be found at https://github.com/CUAI/Non-Homophily-Benchmarks.Comment: In Workshop on Graph Learning Benchmarks (GLB 2021) at WWW 2021. 10
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Adversarial Example Decomposition
Research has shown that widely used deep neural networks are vulnerable to
carefully crafted adversarial perturbations. Moreover, these adversarial
perturbations often transfer across models. We hypothesize that adversarial
weakness is composed of three sources of bias: architecture, dataset, and
random initialization. We show that one can decompose adversarial examples into
an architecture-dependent component, data-dependent component, and
noise-dependent component and that these components behave intuitively. For
example, noise-dependent components transfer poorly to all other models, while
architecture-dependent components transfer better to retrained models with the
same architecture. In addition, we demonstrate that these components can be
recombined to improve transferability without sacrificing efficacy on the
original model.Comment: ICML 2019 Workshop, Security and Privacy of Machine Learning,
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