pwc-archive/papers-with-abstracts huggingface dataset:
On The Power of Curriculum Learning in Training Deep Networks
Authors: Guy Hacohen, Daphna Weinshall
Date: 2019-04-07 00:00:00
Tasks: Transfer Learning

Training neural networks is traditionally done by providing a sequence of random mini-batches sampled uniformly from the entire training data. In this work, we analyze the effect of curriculum learning, which involves the non-uniform sampling of mini-batches, on the training of deep networks, and specifically CNNs trained for image recognition. To employ curriculum learning, the training algorithm must resolve 2 problems: (i) sort the training examples by difficulty; (ii) compute a series of mini-batches that exhibit an increasing level of difficulty. We address challenge (i) using two methods: transfer learning from some competitive ``teacher" network, and bootstrapping. In our empirical evaluation, both methods show similar benefits in terms of increased learning speed and improved final performance on test data. We address challenge (ii) by investigating different pacing functions to guide the sampling. The empirical investigation includes a variety of network architectures, using images from CIFAR-10, CIFAR-100 and subsets of ImageNet. We conclude with a novel theoretical analysis of curriculum learning, where we show how it effectively modifies the optimization landscape. We then define the concept of an ideal curriculum, and show that under mild conditions it does not change the corresponding global minimum of the optimization function.
Learning Metrics from Teachers: Compact Networks for Image Embedding
Authors: Lu Yu, Vacit Oguz Yazici, Xialei Liu, Joost Van de Weijer, Yongmei Cheng, Arnau Ramisa
Date: 2019-04-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Recognition, image-classification, Image Classification, Image Retrieval, Metric Learning, Retrieval

Metric learning networks are used to compute image embeddings, which are
widely used in many applications such as image retrieval and face recognition.
In this paper, we propose to use network distillation to efficiently compute
image embeddings with small networks. Network distillation has been
successfully applied to improve image classification, but has hardly been
explored for metric learning. To do so, we propose two new loss functions that
model the communication of a deep teacher network to a small student network.
We evaluate our system in several datasets, including CUB-200-2011, Cars-196,
Stanford Online Products and show that embeddings computed using small student
networks perform significantly better than those computed using standard
networks of similar size. Results on a very compact network (MobileNet-0.25),
which can be used on mobile devices, show that the proposed method can greatly
improve Recall@1 results from 27.5\% to 44.6\%. Furthermore, we investigate
various aspects of distillation for embeddings, including hint and attention
layers, semi-supervised learning and cross quality distillation. (Code is
available at https://github.com/yulu0724/EmbeddingDistillation.)
Teaching GANs to Sketch in Vector Format
Authors: Varshaneya V, S. Balasubramanian, Vineeth N. Balasubramanian
Date: 2019-04-07 00:00:00
Tasks: Generative Adversarial Network, Reinforcement Learning, Reinforcement Learning (RL)

Sketching is more fundamental to human cognition than speech. Deep Neural
Networks (DNNs) have achieved the state-of-the-art in speech-related tasks but
have not made significant development in generating stroke-based sketches a.k.a
sketches in vector format. Though there are Variational Auto Encoders (VAEs)
for generating sketches in vector format, there is no Generative Adversarial
Network (GAN) architecture for the same. In this paper, we propose a standalone
GAN architecture SkeGAN and a VAE-GAN architecture VASkeGAN, for sketch
generation in vector format. SkeGAN is a stochastic policy in Reinforcement
Learning (RL), capable of generating both multidimensional continuous and
discrete outputs. VASkeGAN hybridizes a VAE and a GAN, in order to couple the
efficient representation of data by VAE with the powerful generating
capabilities of a GAN, to produce visually appealing sketches. We also propose
a new metric called the Ske-score which quantifies the quality of vector
sketches. We have validated that SkeGAN and VASkeGAN generate visually
appealing sketches by using Human Turing Test and Ske-score.
VAE-based regularization for deep speaker embedding
Authors: Yang Zhang, Lantian Li, Dong Wang
Date: 2019-04-07 00:00:00
Tasks: Speaker Recognition

Deep speaker embedding has achieved state-of-the-art performance in speaker
recognition. A potential problem of these embedded vectors (called `x-vectors')
are not Gaussian, causing performance degradation with the famous PLDA back-end
scoring. In this paper, we propose a regularization approach based on
Variational Auto-Encoder (VAE). This model transforms x-vectors to a latent
space where mapped latent codes are more Gaussian, hence more suitable for PLDA
scoring.
A Facial Affect Analysis System for Autism Spectrum Disorder
Authors: Beibin Li, Sachin Mehta, Deepali Aneja, Claire Foster, Pamela Ventola, Frederick Shic, Linda Shapiro
Date: 2019-04-07 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Sensitivity, Specificity

In this paper, we introduce an end-to-end machine learning-based system for
classifying autism spectrum disorder (ASD) using facial attributes such as
expressions, action units, arousal, and valence. Our system classifies ASD
using representations of different facial attributes from convolutional neural
networks, which are trained on images in the wild. Our experimental results
show that different facial attributes used in our system are statistically
significant and improve sensitivity, specificity, and F1 score of ASD
classification by a large margin. In particular, the addition of different
facial attributes improves the performance of ASD classification by about 7%
which achieves a F1 score of 76%.
Recovering Faces from Portraits with Auxiliary Facial Attributes
Authors: Fatemeh Shiri, Xin Yu, Fatih Porikli, Richard Hartley, Piotr Koniusz
Date: 2019-04-07 00:00:00
Tasks: Attribute

Recovering a photorealistic face from an artistic portrait is a challenging
task since crucial facial details are often distorted or completely lost in
artistic compositions. To handle this loss, we propose an Attribute-guided Face
Recovery from Portraits (AFRP) that utilizes a Face Recovery Network (FRN) and
a Discriminative Network (DN). FRN consists of an autoencoder with residual
block-embedded skip-connections and incorporates facial attribute vectors into
the feature maps of input portraits at the bottleneck of the autoencoder. DN
has multiple convolutional and fully-connected layers, and its role is to
enforce FRN to generate authentic face images with corresponding facial
attributes dictated by the input attribute vectors. %Leveraging on the spatial
transformer networks, FRN automatically compensates for misalignments of
portraits. % and generates aligned face images. For the preservation of
identities, we impose the recovered and ground-truth faces to share similar
visual features. Specifically, DN determines whether the recovered image looks
like a real face and checks if the facial attributes extracted from the
recovered image are consistent with given attributes. %Our method can recover
high-quality photorealistic faces from unaligned portraits while preserving the
identity of the face images as well as it can reconstruct a photorealistic face
image with a desired set of attributes. Our method can recover photorealistic
identity-preserving faces with desired attributes from unseen stylized
portraits, artistic paintings, and hand-drawn sketches. On large-scale
synthesized and sketch datasets, we demonstrate that our face recovery method
achieves state-of-the-art results.
Normalized Diversification
Authors: Shaohui Liu, Xiao Zhang, Jianqiao Wangni, Jianbo Shi
Date: 2019-04-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Conditional Image Generation, Diversity, Generative Adversarial Network, Hand Pose Estimation, Image Generation, Pose Estimation, valid

Generating diverse yet specific data is the goal of the generative adversarial network (GAN), but it suffers from the problem of mode collapse. We introduce the concept of normalized diversity which force the model to preserve the normalized pairwise distance between the sparse samples from a latent parametric distribution and their corresponding high-dimensional outputs. The normalized diversification aims to unfold the manifold of unknown topology and non-uniform distribution, which leads to safe interpolation between valid latent variables. By alternating the maximization over the pairwise distance and updating the total distance (normalizer), we encourage the model to actively explore in the high-dimensional output space. We demonstrate that by combining the normalized diversity loss and the adversarial loss, we generate diverse data without suffering from mode collapsing. Experimental results show that our method achieves consistent improvement on unsupervised image generation, conditional image generation and hand pose estimation over strong baselines.
Extending planning knowledge using ontologies for goal opportunities
Authors: Mohannad Babli, Eva Onaindia, Eliseo Marzal
Date: 2019-04-07 00:00:00

Approaches to goal-directed behaviour including online planning and
opportunistic planning tackle a change in the environment by generating
alternative goals to avoid failures or seize opportunities. However, current
approaches only address unanticipated changes related to objects or object
types already defined in the planning task that is being solved. This article
describes a domain-independent approach that advances the state of the art by
extending the knowledge of a planning task with relevant objects of new types.
The approach draws upon the use of ontologies, semantic measures, and ontology
alignment to accommodate newly acquired data that trigger the formulation of
goal opportunities inducing a better-valued plan.
Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction
Authors: Ramy Hussein, Mohamed Osama Ahmed, Rabab Ward, Z. Jane Wang, Levin Kuhlmann, Yi Guo
Date: 2019-04-07 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Prediction, Seizure prediction, Time Series Analysis

Objective: The aim of this study is to develop an efficient and reliable
epileptic seizure prediction system using intracranial EEG (iEEG) data,
especially for people with drug-resistant epilepsy. The prediction procedure
should yield accurate results in a fast enough fashion to alert patients of
impending seizures. Methods: We quantitatively analyze the human iEEG data to
obtain insights into how the human brain behaves before and between epileptic
seizures. We then introduce an efficient pre-processing method for reducing the
data size and converting the time-series iEEG data into an image-like format
that can be used as inputs to convolutional neural networks (CNNs). Further, we
propose a seizure prediction algorithm that uses cooperative multi-scale CNNs
for automatic feature learning of iEEG data. Results: 1) iEEG channels contain
complementary information and excluding individual channels is not advisable to
retain the spatial information needed for accurate prediction of epileptic
seizures. 2) The traditional PCA is not a reliable method for iEEG data
reduction in seizure prediction. 3) Hand-crafted iEEG features may not be
suitable for reliable seizure prediction performance as the iEEG data varies
between patients and over time for the same patient. 4) Seizure prediction
results show that our algorithm outperforms existing methods by achieving an
average sensitivity of 87.85% and AUC score of 0.84. Conclusion: Understanding
how the human brain behaves before seizure attacks and far from them
facilitates better designs of epileptic seizure predictors. Significance:
Accurate seizure prediction algorithms can warn patients about the next seizure
attack so they could avoid dangerous activities. Medications could then be
administered to abort the impending seizure and minimize the risk of injury.
Competitive ratio versus regret minimization: achieving the best of both worlds
Authors: Amit Daniely, Yishay Mansour
Date: 2019-04-07 00:00:00

We consider online algorithms under both the competitive ratio criteria and
the regret minimization one. Our main goal is to build a unified methodology
that would be able to guarantee both criteria simultaneously.
  For a general class of online algorithms, namely any Metrical Task System
(MTS), we show that one can simultaneously guarantee the best known competitive
ratio and a natural regret bound. For the paging problem we further show an
efficient online algorithm (polynomial in the number of pages) with this
guarantee.
  To this end, we extend an existing regret minimization algorithm
(specifically, Kapralov and Panigrahy) to handle movement cost (the cost of
switching between states of the online system). We then show how to use the
extended regret minimization algorithm to combine multiple online algorithms.
Our end result is an online algorithm that can combine a "base" online
algorithm, having a guaranteed competitive ratio, with a range of online
algorithms that guarantee a small regret over any interval of time. The
combined algorithm guarantees both that the competitive ratio matches that of
the base algorithm and a low regret over any time interval.
  As a by product, we obtain an expert algorithm with close to optimal regret
bound on every time interval, even in the presence of switching costs. This
result is of independent interest.
Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics
Authors: Jiangliu Wang, Jianbo Jiao, Linchao Bao, Shengfeng He, Yun-hui Liu, Wei Liu
Date: 2019-04-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, General Classification, Representation Learning, Self-Supervised Action Recognition, Video Classification

We address the problem of video representation learning without
human-annotated labels. While previous efforts address the problem by designing
novel self-supervised tasks using video data, the learned features are merely
on a frame-by-frame basis, which are not applicable to many video analytic
tasks where spatio-temporal features are prevailing. In this paper we propose a
novel self-supervised approach to learn spatio-temporal features for video
representation. Inspired by the success of two-stream approaches in video
classification, we propose to learn visual features by regressing both motion
and appearance statistics along spatial and temporal dimensions, given only the
input video data. Specifically, we extract statistical concepts (fast-motion
region and the corresponding dominant direction, spatio-temporal color
diversity, dominant color, etc.) from simple patterns in both spatial and
temporal domains. Unlike prior puzzles that are even hard for humans to solve,
the proposed approach is consistent with human inherent visual habits and
therefore easy to answer. We conduct extensive experiments with C3D to validate
the effectiveness of our proposed approach. The experiments show that our
approach can significantly improve the performance of C3D when applied to video
classification tasks. Code is available at
https://github.com/laura-wang/video_repres_mas.
Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging
Authors: Sara Meftah, Youssef Tamaazousti, Nasredine Semmar, Hassane Essafi, Fatiha Sadat
Date: 2019-04-07 00:00:00
Proceeding: NAACL 2019 6
Tasks: Domain Adaptation, Part-Of-Speech Tagging, POS, POS Tagging

Fine-tuning neural networks is widely used to transfer valuable knowledge
from high-resource to low-resource domains. In a standard fine-tuning scheme,
source and target problems are trained using the same architecture. Although
capable of adapting to new domains, pre-trained units struggle with learning
uncommon target-specific patterns. In this paper, we propose to augment the
target-network with normalised, weighted and randomly initialised units that
beget a better adaptation while maintaining the valuable source knowledge. Our
experiments on POS tagging of social media texts (Tweets domain) demonstrate
that our method achieves state-of-the-art performances on 3 commonly used
datasets.
On the Convergence Proof of AMSGrad and a New Version
Authors: Tran Thi Phuong, Le Trieu Phong
Date: 2019-04-07 00:00:00

The adaptive moment estimation algorithm Adam (Kingma and Ba) is a popular optimizer in the training of deep neural networks. However, Reddi et al. have recently shown that the convergence proof of Adam is problematic and proposed a variant of Adam called AMSGrad as a fix. In this paper, we show that the convergence proof of AMSGrad is also problematic. Concretely, the problem in the convergence proof of AMSGrad is in handling the hyper-parameters, treating them as equal while they are not. This is also the neglected issue in the convergence proof of Adam. We provide an explicit counter-example of a simple convex optimization setting to show this neglected issue. Depending on manipulating the hyper-parameters, we present various fixes for this issue. We provide a new convergence proof for AMSGrad as the first fix. We also propose a new version of AMSGrad called AdamX as another fix. Our experiments on the benchmark dataset also support our theoretical results.
Modularized Textual Grounding for Counterfactual Resilience
Authors: Zhiyuan Fang, Shu Kong, Charless Fowlkes, Yezhou Yang
Date: 2019-04-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Attribute, counterfactual, Natural Language Visual Grounding, Phrase Grounding, Visual Grounding, Weakly-Supervised Object Localization

Computer Vision applications often require a textual grounding module with precision, interpretability, and resilience to counterfactual inputs/queries. To achieve high grounding precision, current textual grounding methods heavily rely on large-scale training data with manual annotations at the pixel level. Such annotations are expensive to obtain and thus severely narrow the model's scope of real-world applications. Moreover, most of these methods sacrifice interpretability, generalizability, and they neglect the importance of being resilient to counterfactual inputs. To address these issues, we propose a visual grounding system which is 1) end-to-end trainable in a weakly supervised fashion with only image-level annotations, and 2) counterfactually resilient owing to the modular design. Specifically, we decompose textual descriptions into three levels: entity, semantic attribute, color information, and perform compositional grounding progressively. We validate our model through a series of experiments and demonstrate its improvement over the state-of-the-art methods. In particular, our model's performance not only surpasses other weakly/un-supervised methods and even approaches the strongly supervised ones, but also is interpretable for decision making and performs much better in face of counterfactual classes than all the others.
Reframing Threat Detection: Inside esINSIDER
Authors: M. Arthur Munson, Jason Kichen, Dustin Hillard, Ashley Fidler, Peiter Zatko
Date: 2019-04-07 00:00:00
Tasks: BIG-bench Machine Learning

We describe the motivation and design for esINSIDER, an automated tool that
detects potential persistent and insider threats in a network. esINSIDER
aggregates clues from log data, over extended time periods, and proposes a
small number of cases for human experts to review. The proposed cases package
together related information so the analyst can see a bigger picture of what is
happening, and their evidence includes internal network activity resembling
reconnaissance and data collection.
  The core ideas are to 1) detect fundamental campaign behaviors by following
data movements over extended time periods, 2) link together behaviors
associated with different meta-goals, and 3) use machine learning to understand
what activities are expected and consistent for each individual network. We
call this approach campaign analytics because it focuses on the threat actor's
campaign goals and the intrinsic steps to achieve them. Linking different
campaign behaviors (internal reconnaissance, collection, exfiltration) reduces
false positives from business-as-usual activities and creates opportunities to
detect threats before a large exfiltration occurs. Machine learning makes it
practical to deploy this approach by reducing the amount of tuning needed.
Multi-Label Image Recognition with Graph Convolutional Networks
Authors: Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, Yanwen Guo
Date: 2019-04-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Long-tail Learning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Image Recognition, Object, Word Embeddings

The task of multi-label image recognition is to predict a set of object
labels that present in an image. As objects normally co-occur in an image, it
is desirable to model the label dependencies to improve the recognition
performance. To capture and explore such important dependencies, we propose a
multi-label classification model based on Graph Convolutional Network (GCN).
The model builds a directed graph over the object labels, where each node
(label) is represented by word embeddings of a label, and GCN is learned to map
this label graph into a set of inter-dependent object classifiers. These
classifiers are applied to the image descriptors extracted by another sub-net,
enabling the whole network to be end-to-end trainable. Furthermore, we propose
a novel re-weighted scheme to create an effective label correlation matrix to
guide information propagation among the nodes in GCN. Experiments on two
multi-label image recognition datasets show that our approach obviously
outperforms other existing state-of-the-art methods. In addition, visualization
analyses reveal that the classifiers learned by our model maintain meaningful
semantic topology.
Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning
Authors: Huaxi Huang, Jun-Jie Zhang, Jian Zhang, Qiang Wu, Jingsong Xu
Date: 2019-04-07 00:00:00
Tasks: General Classification, Meta-Learning

The recognition ability of human beings is developed in a progressive way.
Usually, children learn to discriminate various objects from coarse to
fine-grained with limited supervision. Inspired by this learning process, we
propose a simple yet effective model for the Few-Shot Fine-Grained (FSFG)
recognition, which tries to tackle the challenging fine-grained recognition
task using meta-learning. The proposed method, named Pairwise Alignment
Bilinear Network (PABN), is an end-to-end deep neural network. Unlike
traditional deep bilinear networks for fine-grained classification, which adopt
the self-bilinear pooling to capture the subtle features of images, the
proposed model uses a novel pairwise bilinear pooling to compare the nuanced
differences between base images and query images for learning a deep distance
metric. In order to match base image features with query image features, we
design feature alignment losses before the proposed pairwise bilinear pooling.
Experiment results on four fine-grained classification datasets and one generic
few-shot dataset demonstrate that the proposed model outperforms both the
state-ofthe-art few-shot fine-grained and general few-shot methods.