pwc-archive/papers-with-abstracts huggingface dataset:
Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints
Authors: Ning Yu, Larry Davis, Mario Fritz
Date: 2018-11-20 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Attribution, Image Generation

Recent advances in Generative Adversarial Networks (GANs) have shown increasing success in generating photorealistic images. But they also raise challenges to visual forensics and model attribution. We present the first study of learning GAN fingerprints towards image attribution and using them to classify an image as real or GAN-generated. For GAN-generated images, we further identify their sources. Our experiments show that (1) GANs carry distinct model fingerprints and leave stable fingerprints in their generated images, which support image attribution; (2) even minor differences in GAN training can result in different fingerprints, which enables fine-grained model authentication; (3) fingerprints persist across different image frequencies and patches and are not biased by GAN artifacts; (4) fingerprint finetuning is effective in immunizing against five types of adversarial image perturbations; and (5) comparisons also show our learned fingerprints consistently outperform several baselines in a variety of setups.
Sketch-R2CNN: An Attentive Network for Vector Sketch Recognition
Authors: Lei Li, Changqing Zou, Youyi Zheng, Qingkun Su, Hongbo Fu, Chiew-Lan Tai
Date: 2018-11-20 00:00:00
Tasks: Sketch Recognition

Freehand sketching is a dynamic process where points are sequentially sampled
and grouped as strokes for sketch acquisition on electronic devices. To
recognize a sketched object, most existing methods discard such important
temporal ordering and grouping information from human and simply rasterize
sketches into binary images for classification. In this paper, we propose a
novel single-branch attentive network architecture RNN-Rasterization-CNN
(Sketch-R2CNN for short) to fully leverage the dynamics in sketches for
recognition. Sketch-R2CNN takes as input only a vector sketch with grouped
sequences of points, and uses an RNN for stroke attention estimation in the
vector space and a CNN for 2D feature extraction in the pixel space
respectively. To bridge the gap between these two spaces in neural networks, we
propose a neural line rasterization module to convert the vector sketch along
with the attention estimated by RNN into a bitmap image, which is subsequently
consumed by CNN. The neural line rasterization module is designed in a
differentiable way to yield a unified pipeline for end-to-end learning. We
perform experiments on existing large-scale sketch recognition benchmarks and
show that by exploiting the sketch dynamics with the attention mechanism, our
method is more robust and achieves better performance than the state-of-the-art
methods.
Unsupervised Learning of Shape Concepts - From Real-World Objects to Mental Simulation
Authors: Christian A. Mueller, Andreas Birk
Date: 2018-11-20 00:00:00
Tasks: Dataset Generation

An unsupervised shape analysis is proposed to learn concepts reflecting shape
commonalities. Our approach is two-fold: i) a spatial topology analysis of
point cloud segment constellations within objects is used in which
constellations are decomposed and described in a hierarchical and symbolic
manner. ii) A topology analysis of the description space is used in which
segment decompositions are exposed in. Inspired by Persistent Homology, groups
of shape commonality are revealed. Experiments show that extracted persistent
commonality groups can feature semantically meaningful shape concepts; the
generalization of the proposed approach is evaluated by different real-world
datasets. We extend this by not only learning shape concepts using real-world
data, but by also using mental simulation of artificial abstract objects for
training purposes. This extended approach is unsupervised in two respects:
label-agnostic (no label information is used) and instance-agnostic (no
instances preselected by human supervision are used for training). Experiments
show that concepts generated with mental simulation, generalize and
discriminate real object observations. Consequently, a robot may train and
learn its own internal representation of concepts regarding shape appearance in
a self-driven and machine-centric manner while omitting the tedious process of
supervised dataset generation including the ambiguity in instance labeling and
selection.
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging
Authors: Qingjie Meng, Matthew Sinclair, Veronika Zimmer, Benjamin Hou, Martin Rajchl, Nicolas Toussaint, Ozan Oktay, Jo Schlemper, Alberto Gomez, James Housden, Jacqueline Matthew, Daniel Rueckert, Julia Schnabel, Bernhard Kainz
Date: 2018-11-20 00:00:00
Tasks: Diagnostic, image-classification, Image Classification, Segmentation, Shadow Confidence Maps In Ultrasound Imaging

Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provide additional information for other automatic image analysis algorithms. However, automatically detecting shadow regions using learning-based algorithms is challenging because pixel-wise ground truth annotation of acoustic shadows is subjective and time consuming. In this paper we propose a weakly supervised method for automatic confidence estimation of acoustic shadow regions. Our method is able to generate a dense shadow-focused confidence map. In our method, a shadow-seg module is built to learn general shadow features for shadow segmentation, based on global image-level annotations as well as a small number of coarse pixel-wise shadow annotations. A transfer function is introduced to extend the obtained binary shadow segmentation to a reference confidence map. Additionally, a confidence estimation network is proposed to learn the mapping between input images and the reference confidence maps. This network is able to predict shadow confidence maps directly from input images during inference. We use evaluation metrics such as DICE, inter-class correlation and etc. to verify the effectiveness of our method. Our method is more consistent than human annotation, and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions. We further demonstrate the applicability of our method by integrating shadow confidence maps into tasks such as ultrasound image classification, multi-view image fusion and automated biometric measurements.
Variance Suppression: Balanced Training Process in Deep Learning
Authors: Tao Yi, Xingxuan Wang
Date: 2018-11-20 00:00:00
Tasks: Deep Learning

Stochastic gradient descent updates parameters with summation gradient
computed from a random data batch. This summation will lead to unbalanced
training process if the data we obtained is unbalanced. To address this issue,
this paper takes the error variance and error mean both into consideration. The
adaptively adjusting approach of two terms trading off is also given in our
algorithm. Due to this algorithm can suppress error variance, we named it
Variance Suppression Gradient Descent (VSSGD). Experimental results have
demonstrated that VSSGD can accelerate the training process, effectively
prevent overfitting, improve the networks learning capacity from small samples.
DeepZip: Lossless Data Compression using Recurrent Neural Networks
Authors: Mohit Goyal, Kedar Tatwawadi, Shubham Chandak, Idoia Ochoa
Date: 2018-11-20 00:00:00
Tasks: Data Compression

Sequential data is being generated at an unprecedented pace in various forms,
including text and genomic data. This creates the need for efficient
compression mechanisms to enable better storage, transmission and processing of
such data. To solve this problem, many of the existing compressors attempt to
learn models for the data and perform prediction-based compression. Since
neural networks are known as universal function approximators with the
capability to learn arbitrarily complex mappings, and in practice show
excellent performance in prediction tasks, we explore and devise methods to
compress sequential data using neural network predictors. We combine recurrent
neural network predictors with an arithmetic coder and losslessly compress a
variety of synthetic, text and genomic datasets. The proposed compressor
outperforms Gzip on the real datasets and achieves near-optimal compression for
the synthetic datasets. The results also help understand why and where neural
networks are good alternatives for traditional finite context models
Machine Learning Distinguishes Neurosurgical Skill Levels in a Virtual Reality Tumor Resection Task
Authors: Samaneh Siyar, Hamed Azarnoush, Saeid Rashidi, Alexandre Winkler-Schwartz, Vincent Bissonnette, Nirros Ponnudurai, Rolando F. Del Maestro
Date: 2018-11-20 00:00:00
Tasks: BIG-bench Machine Learning, feature selection

Background: Virtual reality simulators and machine learning have the
potential to augment understanding, assessment and training of psychomotor
performance in neurosurgery residents. Objective: This study outlines the first
application of machine learning to distinguish "skilled" and "novice"
psychomotor performance during a virtual reality neurosurgical task. Methods:
Twenty-three neurosurgeons and senior neurosurgery residents comprising the
"skilled" group and 92 junior neurosurgery residents and medical students the
"novice" group. The task involved removing a series of virtual brain tumors
without causing injury to surrounding tissue. Over 100 features were extracted
and 68 selected using t-test analysis. These features were provided to 4
classifiers: K-Nearest Neighbors, Parzen Window, Support Vector Machine, and
Fuzzy K-Nearest Neighbors. Equal Error Rate was used to assess classifier
performance. Results: Ratios of train set size to test set size from 10% to 90%
and 5 to 30 features, chosen by the forward feature selection algorithm, were
employed. A working point of 50% train to test set size ratio and 15 features
resulted in an equal error rates as low as 8.3% using the Fuzzy K-Nearest
Neighbors classifier. Conclusion: Machine learning may be one component helping
realign the traditional apprenticeship educational paradigm to a more objective
model based on proven performance standards.
  Keywords: Artificial intelligence, Classifiers, Machine learning,
Neurosurgery skill assessment, Surgical education, Tumor resection, Virtual
reality simulation
How You See Me
Authors: Rohit Gandikota, Deepak Mishra
Date: 2018-11-20 00:00:00
Tasks: Math

Convolution Neural Networks is one of the most powerful tools in the present
era of science. There has been a lot of research done to improve their
performance and robustness while their internal working was left unexplored to
much extent. They are often defined as black boxes that can map non-linear data
very effectively. This paper tries to show how CNN has learned to look at an
image. The proposed algorithm exploits the basic math of CNN to backtrack the
important pixels it is considering to predict. This is a simple algorithm which
does not involve any training of its own over a pre-trained CNN which can
classify.
Effect of Depth and Width on Local Minima in Deep Learning
Authors: Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling
Date: 2018-11-20 00:00:00

In this paper, we analyze the effects of depth and width on the quality of local minima, without strong over-parameterization and simplification assumptions in the literature. Without any simplification assumption, for deep nonlinear neural networks with the squared loss, we theoretically show that the quality of local minima tends to improve towards the global minimum value as depth and width increase. Furthermore, with a locally-induced structure on deep nonlinear neural networks, the values of local minima of neural networks are theoretically proven to be no worse than the globally optimal values of corresponding classical machine learning models. We empirically support our theoretical observation with a synthetic dataset as well as MNIST, CIFAR-10 and SVHN datasets. When compared to previous studies with strong over-parameterization assumptions, the results in this paper do not require over-parameterization, and instead show the gradual effects of over-parameterization as consequences of general results.
Adversarial point set registration
Authors: Sergei Divakov, Ivan Oseledets
Date: 2018-11-20 00:00:00

We present a novel approach to point set registration which is based on
one-shot adversarial learning. The idea of the algorithm is inspired by recent
successes of generative adversarial networks. Treating the point clouds as
three-dimensional probability distributions, we develop a one-shot adversarial
optimization procedure, in which we train a critic neural network to
distinguish between source and target point sets, while simultaneously learning
the parameters of the transformation to trick the critic into confusing the
points. In contrast to most existing algorithms for point set registration,
ours does not rely on any correspondences between the point clouds. We
demonstrate the performance of the algorithm on several challenging benchmarks
and compare it to the existing baselines.
Learning to Detect Instantaneous Changes with Retrospective Convolution and Static Sample Synthesis
Authors: Chao Chen, Sheng Zhang, Cuibing Du
Date: 2018-11-20 00:00:00
Tasks: Change Detection, Data Augmentation

Change detection has been a challenging visual task due to the dynamic nature
of real-world scenes. Good performance of existing methods depends largely on
prior background images or a long-term observation. These methods, however,
suffer severe degradation when they are applied to detection of instantaneously
occurred changes with only a few preceding frames provided. In this paper, we
exploit spatio-temporal convolutional networks to address this challenge, and
propose a novel retrospective convolution, which features efficient change
information extraction between the current frame and frames from historical
observation. To address the problem of foreground-specific over-fitting in
learning-based methods, we further propose a data augmentation method, named
static sample synthesis, to guide the network to focus on learning change-cued
information rather than specific spatial features of foreground. Trained
end-to-end with complex scenarios, our framework proves to be accurate in
detecting instantaneous changes and robust in combating diverse noises.
Extensive experiments demonstrate that our proposed method significantly
outperforms existing methods.
Alignment Analysis of Sequential Segmentation of Lexicons to Improve Automatic Cognate Detection
Authors: Pranav A
Date: 2018-11-20 00:00:00
Proceeding: ACL 2018 7
Tasks: General Classification, Information Retrieval, Language Modelling, Retrieval, Segmentation

Ranking functions in information retrieval are often used in search engines
to recommend the relevant answers to the query. This paper makes use of this
notion of information retrieval and applies onto the problem domain of cognate
detection. The main contributions of this paper are: (1) positional
segmentation, which incorporates the sequential notion; (2) graphical error
modelling, which deduces the transformations. The current research work focuses
on classification problem; which is distinguishing whether a pair of words are
cognates. This paper focuses on a harder problem, whether we could predict a
possible cognate from the given input. Our study shows that when language
modelling smoothing methods are applied as the retrieval functions and used in
conjunction with positional segmentation and error modelling gives better
results than competing baselines, in both classification and prediction of
cognates.
  Source code is at: https://github.com/pranav-ust/cognates
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables
Authors: Alireza Abedin Varamin, Ehsan Abbasnejad, Qinfeng Shi, Damith Ranasinghe, Hamid Rezatofighi
Date: 2018-11-20 00:00:00
Tasks: Activity Recognition, Multi-class Classification, Time Series, Time Series Analysis

Automatic recognition of human activities from time-series sensor data
(referred to as HAR) is a growing area of research in ubiquitous computing.
Most recent research in the field adopts supervised deep learning paradigms to
automate extraction of intrinsic features from raw signal inputs and addresses
HAR as a multi-class classification problem where detecting a single activity
class within the duration of a sensory data segment suffices. However, due to
the innate diversity of human activities and their corresponding duration, no
data segment is guaranteed to contain sensor recordings of a single activity
type. In this paper, we express HAR more naturally as a set prediction problem
where the predictions are sets of ongoing activity elements with unfixed and
unknown cardinality. For the first time, we address this problem by presenting
a novel HAR approach that learns to output activity sets using deep neural
networks. Moreover, motivated by the limited availability of annotated HAR
datasets as well as the unfortunate immaturity of existing unsupervised
systems, we complement our supervised set learning scheme with a prior
unsupervised feature learning process that adopts convolutional auto-encoders
to exploit unlabeled data. The empirical experiments on two widely adopted HAR
datasets demonstrate the substantial improvement of our proposed methodology
over the baseline models.
Adversarial Feedback Loop
Authors: Firas Shama, Roey Mechrez, Alon Shoshan, Lihi Zelnik-Manor
Date: 2018-11-20 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Generation, Super-Resolution

Thanks to their remarkable generative capabilities, GANs have gained great
popularity, and are used abundantly in state-of-the-art methods and
applications. In a GAN based model, a discriminator is trained to learn the
real data distribution. To date, it has been used only for training purposes,
where it's utilized to train the generator to provide real-looking outputs. In
this paper we propose a novel method that makes an explicit use of the
discriminator in test-time, in a feedback manner in order to improve the
generator results. To the best of our knowledge it is the first time a
discriminator is involved in test-time. We claim that the discriminator holds
significant information on the real data distribution, that could be useful for
test-time as well, a potential that has not been explored before.
  The approach we propose does not alter the conventional training stage. At
test-time, however, it transfers the output from the generator into the
discriminator, and uses feedback modules (convolutional blocks) to translate
the features of the discriminator layers into corrections to the features of
the generator layers, which are used eventually to get a better generator
result. Our method can contribute to both conditional and unconditional GANs.
As demonstrated by our experiments, it can improve the results of
state-of-the-art networks for super-resolution, and image generation.
Explaining Latent Factor Models for Recommendation with Influence Functions
Authors: Weiyu Cheng, Yanyan Shen, Yanmin Zhu, Linpeng Huang
Date: 2018-11-20 00:00:00
Tasks: Collaborative Filtering

Latent factor models (LFMs) such as matrix factorization achieve the
state-of-the-art performance among various Collaborative Filtering (CF)
approaches for recommendation. Despite the high recommendation accuracy of
LFMs, a critical issue to be resolved is the lack of explainability. Extensive
efforts have been made in the literature to incorporate explainability into
LFMs. However, they either rely on auxiliary information which may not be
available in practice, or fail to provide easy-to-understand explanations. In
this paper, we propose a fast influence analysis method named FIA, which
successfully enforces explicit neighbor-style explanations to LFMs with the
technique of influence functions stemmed from robust statistics. We first
describe how to employ influence functions to LFMs to deliver neighbor-style
explanations. Then we develop a novel influence computation algorithm for
matrix factorization with high efficiency. We further extend it to the more
general neural collaborative filtering and introduce an approximation algorithm
to accelerate influence analysis over neural network models. Experimental
results on real datasets demonstrate the correctness, efficiency and usefulness
of our proposed method.
Limited Gradient Descent: Learning With Noisy Labels
Authors: Yi Sun, Yan Tian, Yiping Xu, Jianxiang Li
Date: 2018-11-20 00:00:00
Tasks: Learning with noisy labels, Memorization

Label noise may affect the generalization of classifiers, and the effective learning of main patterns from samples with noisy labels is an important challenge. Recent studies have shown that deep neural networks tend to prioritize the learning of simple patterns over the memorization of noise patterns. This suggests a possible method to search for the best generalization that learns the main pattern until the noise begins to be memorized. Traditional approaches often employ a clean validation set to find the best stop timing of learning, i.e., early stopping. However, the generalization performance of such methods relies on the quality of validation sets. Further, in practice, a clean validation set is sometimes difficult to obtain. To solve this problem, we propose a method that can estimate the optimal stopping timing without a clean validation set, called limited gradient descent. We modified the labels of a few samples in a noisy dataset to obtain false labels and to create a reverse pattern. By monitoring the learning progress of the noisy and reverse samples, we can determine the stop timing of learning. In this paper, we also theoretically provide some necessary conditions on learning with noisy labels. Experimental results on CIFAR-10 and CIFAR-100 datasets demonstrate that our approach has a comparable generalization performance to methods relying on a clean validation set. Thus, on the noisy Clothing-1M dataset, our approach surpasses methods that rely on a clean validation set.
Sequence-based Person Attribute Recognition with Joint CTC-Attention Model
Authors: Hao Liu, Jingjing Wu, Jianguo Jiang, Meibin Qi, Bo Ren
Date: 2018-11-20 00:00:00
Tasks: Attribute, Object Recognition, Person Re-Identification

Attribute recognition has become crucial because of its wide applications in
many computer vision tasks, such as person re-identification. Like many object
recognition problems, variations in viewpoints, illumination, and recognition
at far distance, all make this task challenging. In this work, we propose a
joint CTC-Attention model (JCM), which maps attribute labels into sequences to
learn the semantic relationship among attributes. Besides, this network uses
neural network to encode images into sequences, and employs connectionist
temporal classification (CTC) loss to train the network with the aim of
improving the encoding performance of the network. At the same time, it adopts
the attention model to decode the sequences, which can realize aligning the
sequences and better learning the semantic information from attributes.
Extensive experiments on three public datasets, i.e., Market-1501 attribute
dataset, Duke attribute dataset and PETA dataset, demonstrate the effectiveness
of the proposed method.