pwc-archive/papers-with-abstracts huggingface dataset:
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
Authors: Yigitcan Kaya, Sanghyun Hong, Tudor Dumitras
Date: 2018-10-16 00:00:00
Tasks: image-classification, Image Classification, Prediction

We characterize a prevalent weakness of deep neural networks (DNNs)---overthinking---which occurs when a DNN can reach correct predictions before its final layer. Overthinking is computationally wasteful, and it can also be destructive when, by the final layer, a correct prediction changes into a misclassification. Understanding overthinking requires studying how each prediction evolves during a DNN's forward pass, which conventionally is opaque. For prediction transparency, we propose the Shallow-Deep Network (SDN), a generic modification to off-the-shelf DNNs that introduces internal classifiers. We apply SDN to four modern architectures, trained on three image classification tasks, to characterize the overthinking problem. We show that SDNs can mitigate the wasteful effect of overthinking with confidence-based early exits, which reduce the average inference cost by more than 50% and preserve the accuracy. We also find that the destructive effect occurs for 50% of misclassifications on natural inputs and that it can be induced, adversarially, with a recent backdooring attack. To mitigate this effect, we propose a new confusion metric to quantify the internal disagreements that will likely lead to misclassifications.
Generating Self-Guided Dense Annotations for Weakly Supervised Semantic Segmentation
Authors: Zi-Yi Ke, Chiou-Ting Hsu
Date: 2018-10-16 00:00:00
Tasks: Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Learning semantic segmentation models under image-level supervision is far
more challenging than under fully supervised setting. Without knowing the exact
pixel-label correspondence, most weakly-supervised methods rely on external
models to infer pseudo pixel-level labels for training semantic segmentation
models. In this paper, we aim to develop a single neural network without
resorting to any external models. We propose a novel self-guided strategy to
fully utilize features learned across multiple levels to progressively generate
the dense pseudo labels. First, we use high-level features as class-specific
localization maps to roughly locate the classes. Next, we propose an
affinity-guided method to encourage each localization map to be consistent with
their intermediate level features. Third, we adopt the training image itself as
guidance and propose a self-guided refinement to further transfer the image's
inherent structure into the maps. Finally, we derive pseudo pixel-level labels
from these localization maps and use the pseudo labels as ground truth to train
the semantic segmentation model. Our proposed self-guided strategy is a unified
framework, which is built on a single network and alternatively updates the
feature representation and refines localization maps during the training
procedure. Experimental results on PASCAL VOC 2012 segmentation benchmark
demonstrate that our method outperforms other weakly-supervised methods under
the same setting.
Learning Inward Scaled Hypersphere Embedding: Exploring Projections in Higher Dimensions
Authors: Muhammad Kamran Janjua, Shah Nawaz, Alessandro Calefati, Ignazio Gallo
Date: 2018-10-16 00:00:00
Tasks: Dimensionality Reduction, General Classification, Retrieval

Majority of the current dimensionality reduction or retrieval techniques rely
on embedding the learned feature representations onto a computable metric
space. Once the learned features are mapped, a distance metric aids the
bridging of gaps between similar instances. Since the scaled projection is not
exploited in these methods, discriminative embedding onto a hyperspace becomes
a challenge. In this paper, we propose to inwardly scale feature
representations in proportional to projecting them onto a hypersphere manifold
for discriminative analysis. We further propose a novel, yet simpler,
convolutional neural network based architecture and extensively evaluate the
proposed methodology in the context of classification and retrieval tasks
obtaining results comparable to state-of-the-art techniques.
Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities
Authors: Jose Dolz, Ismail Ben Ayed, Christian Desrosiers
Date: 2018-10-16 00:00:00
Tasks: GPU, Ischemic Stroke Lesion Segmentation, Lesion Segmentation

Delineating infarcted tissue in ischemic stroke lesions is crucial to
determine the extend of damage and optimal treatment for this life-threatening
condition. However, this problem remains challenging due to high variability of
ischemic strokes' location and shape. Recently, fully-convolutional neural
networks (CNN), in particular those based on U-Net, have led to improved
performances for this task. In this work, we propose a novel architecture that
improves standard U-Net based methods in three important ways. First, instead
of combining the available image modalities at the input, each of them is
processed in a different path to better exploit their unique information.
Moreover, the network is densely-connected (i.e., each layer is connected to
all following layers), both within each path and across different paths,
similar to HyperDenseNet. This gives our model the freedom to learn the scale
at which modalities should be processed and combined. Finally, inspired by the
Inception architecture, we improve standard U-Net modules by extending
inception modules with two convolutional blocks with dilated convolutions of
different scale. This helps handling the variability in lesion sizes. We split
the 93 stroke datasets into training and validation sets containing 83 and 9
examples respectively. Our network was trained on a NVidia TITAN XP GPU with 16
GBs RAM, using ADAM as optimizer and a learning rate of 1$\times$10$^{-5}$
during 200 epochs. Training took around 5 hours and segmentation of a whole
volume took between 0.2 and 2 seconds, as average. The performance on the test
set obtained by our method is compared to several baselines, to demonstrate the
effectiveness of our architecture, and to a state-of-art architecture that
employs factorized dilated convolutions, i.e., ERFNet.
Efficient Greedy Coordinate Descent for Composite Problems
Authors: Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
Date: 2018-10-16 00:00:00

Coordinate descent with random coordinate selection is the current state of
the art for many large scale optimization problems. However, greedy selection
of the steepest coordinate on smooth problems can yield convergence rates
independent of the dimension $n$, and requiring upto $n$ times fewer
iterations.
  In this paper, we consider greedy updates that are based on subgradients for
a class of non-smooth composite problems, which includes $L1$-regularized
problems, SVMs and related applications. For these problems we provide (i) the
first linear rates of convergence independent of $n$, and show that our greedy
update rule provides speedups similar to those obtained in the smooth case.
This was previously conjectured to be true for a stronger greedy coordinate
selection strategy.
  Furthermore, we show that (ii) our new selection rule can be mapped to
instances of maximum inner product search, allowing to leverage standard
nearest neighbor algorithms to speed up the implementation. We demonstrate the
validity of the approach through extensive numerical experiments.
SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification
Authors: Xing Fan, Hao Luo, Xuan Zhang, Lingxiao He, Chi Zhang, Wei Jiang
Date: 2018-10-16 00:00:00
Tasks: Person Re-Identification

Holistic person re-identification (ReID) has received extensive study in the
past few years and achieves impressive progress. However, persons are often
occluded by obstacles or other persons in practical scenarios, which makes
partial person re-identification non-trivial. In this paper, we propose a
spatial-channel parallelism network (SCPNet) in which each channel in the ReID
feature pays attention to a given spatial part of the body. The spatial-channel
corresponding relationship supervises the network to learn discriminative
feature for both holistic and partial person re-identification. The single
model trained on four holistic ReID datasets achieves competitive accuracy on
these four datasets, as well as outperforms the state-of-the-art methods on two
partial ReID datasets without training.
LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild
Authors: Shuang Yang, Yuan-Hang Zhang, Dalu Feng, Mingmin Yang, Chenhao Wang, Jing-Yun Xiao, Keyu Long, Shiguang Shan, Xilin Chen
Date: 2018-10-16 00:00:00
Tasks: Lipreading, Lip Reading, speech-recognition, Speech Recognition, Visual Speech Recognition

Large-scale datasets have successively proven their fundamental importance in
several research fields, especially for early progress in some emerging topics.
In this paper, we focus on the problem of visual speech recognition, also known
as lipreading, which has received increasing interest in recent years. We
present a naturally-distributed large-scale benchmark for lip reading in the
wild, named LRW-1000, which contains 1,000 classes with 718,018 samples from
more than 2,000 individual speakers. Each class corresponds to the syllables of
a Mandarin word composed of one or several Chinese characters. To the best of
our knowledge, it is currently the largest word-level lipreading dataset and
also the only public large-scale Mandarin lipreading dataset. This dataset aims
at covering a "natural" variability over different speech modes and imaging
conditions to incorporate challenges encountered in practical applications. It
has shown a large variation in this benchmark in several aspects, including the
number of samples in each class, video resolution, lighting conditions, and
speakers' attributes such as pose, age, gender, and make-up. Besides providing
a detailed description of the dataset and its collection pipeline, we evaluate
several typical popular lipreading methods and perform a thorough analysis of
the results from several aspects. The results demonstrate the consistency and
challenges of our dataset, which may open up some new promising directions for
future work.
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
Authors: Kaspar Märtens, Kieran R. Campbell, Christopher Yau
Date: 2018-10-16 00:00:00
Tasks: Dimensionality Reduction

The interpretation of complex high-dimensional data typically requires the use of dimensionality reduction techniques to extract explanatory low-dimensional representations. However, in many real-world problems these representations may not be sufficient to aid interpretation on their own, and it would be desirable to interpret the model in terms of the original features themselves. Our goal is to characterise how feature-level variation depends on latent low-dimensional representations, external covariates, and non-linear interactions between the two. In this paper, we propose to achieve this through a structured kernel decomposition in a hybrid Gaussian Process model which we call the Covariate Gaussian Process Latent Variable Model (c-GPLVM). We demonstrate the utility of our model on simulated examples and applications in disease progression modelling from high-dimensional gene expression data in the presence of additional phenotypes. In each setting we show how the c-GPLVM can extract low-dimensional structures from high-dimensional data sets whilst allowing a breakdown of feature-level variability that is not present in other commonly used dimensionality reduction approaches.
Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning
Authors: Yuan Gao, Fangkai Yang, Martin Frisk, Daniel Hernandez, Christopher Peters, Ginevra Castellano
Date: 2018-10-16 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning has recently been widely applied in robotics to study tasks such as locomotion and grasping, but its application to social human-robot interaction (HRI) remains a challenge. In this paper, we present a deep learning scheme that acquires a prior model of robot approaching behavior in simulation and applies it to real-world interaction with a physical robot approaching groups of humans. The scheme, which we refer to as Staged Social Behavior Learning (SSBL), considers different stages of learning in social scenarios. We learn robot approaching behaviors towards small groups in simulation and evaluate the performance of the model using objective and subjective measures in a perceptual study and a HRI user study with human participants. Results show that our model generates more socially appropriate behavior compared to a state-of-the-art model.
Deep Metric Learning with Hierarchical Triplet Loss
Authors: Weifeng Ge, Weilin Huang, Dengke Dong, Matthew R. Scott
Date: 2018-10-16 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face Recognition, Image Retrieval, Metric Learning, Retrieval, Triplet

We present a novel hierarchical triplet loss (HTL) capable of automatically
collecting informative training samples (triplets) via a defined hierarchical
tree that encodes global context information. This allows us to cope with the
main limitation of random sampling in training a conventional triplet loss,
which is a central issue for deep metric learning. Our main contributions are
two-fold. (i) we construct a hierarchical class-level tree where neighboring
classes are merged recursively. The hierarchical structure naturally captures
the intrinsic data distribution over the whole database. (ii) we formulate the
problem of triplet collection by introducing a new violate margin, which is
computed dynamically based on the designed hierarchical tree. This allows it to
automatically select meaningful hard samples with the guide of global context.
It encourages the model to learn more discriminative features from visual
similar classes, leading to faster convergence and better performance. Our
method is evaluated on the tasks of image retrieval and face recognition, where
it outperforms the standard triplet loss substantially by 1%-18%. It achieves
new state-of-the-art performance on a number of benchmarks, with much fewer
learning iterations.
The Deep Weight Prior
Authors: Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitry Vetrov, Max Welling
Date: 2018-10-16 00:00:00
Proceeding: ICLR 2019 5
Tasks: Bayesian Inference, Variational Inference

Bayesian inference is known to provide a general framework for incorporating
prior knowledge or specific properties into machine learning models via
carefully choosing a prior distribution. In this work, we propose a new type of
prior distributions for convolutional neural networks, deep weight prior (DWP),
that exploit generative models to encourage a specific structure of trained
convolutional filters e.g., spatial correlations of weights. We define DWP in
the form of an implicit distribution and propose a method for variational
inference with such type of implicit priors. In experiments, we show that DWP
improves the performance of Bayesian neural networks when training data are
limited, and initialization of weights with samples from DWP accelerates
training of conventional convolutional neural networks.
UnrealROX: An eXtremely Photorealistic Virtual Reality Environment for Robotics Simulations and Synthetic Data Generation
Authors: Pablo Martinez-Gonzalez, Sergiu Oprea, Alberto Garcia-Garcia, Alvaro Jover-Alvarez, Sergio Orts-Escolano, Jose Garcia-Rodriguez
Date: 2018-10-16 00:00:00
Tasks: Depth Estimation, object-detection, Object Detection, Semantic Segmentation, Synthetic Data Generation

Data-driven algorithms have surpassed traditional techniques in almost every aspect in robotic vision problems. Such algorithms need vast amounts of quality data to be able to work properly after their training process. Gathering and annotating that sheer amount of data in the real world is a time-consuming and error-prone task. Those problems limit scale and quality. Synthetic data generation has become increasingly popular since it is faster to generate and automatic to annotate. However, most of the current datasets and environments lack realism, interactions, and details from the real world. UnrealROX is an environment built over Unreal Engine 4 which aims to reduce that reality gap by leveraging hyperrealistic indoor scenes that are explored by robot agents which also interact with objects in a visually realistic manner in that simulated world. Photorealistic scenes and robots are rendered by Unreal Engine into a virtual reality headset which captures gaze so that a human operator can move the robot and use controllers for the robotic hands; scene information is dumped on a per-frame basis so that it can be reproduced offline to generate raw data and ground truth annotations. This virtual reality environment enables robotic vision researchers to generate realistic and visually plausible data with full ground truth for a wide variety of problems such as class and instance semantic segmentation, object detection, depth estimation, visual grasping, and navigation.
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution
Authors: Yue Lu, Yun Zhou, Zhuqing Jiang, Xiaoqiang Guo, Zixuan Yang
Date: 2018-10-16 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Convolutional neural networks (CNNs) have demonstrated superior performance
in super-resolution (SR). However, most CNN-based SR methods neglect the
different importance among feature channels or fail to take full advantage of
the hierarchical features. To address these issues, this paper presents a novel
recursive unit. Firstly, at the beginning of each unit, we adopt a compact
channel attention mechanism to adaptively recalibrate the channel importance of
input features. Then, the multi-level features, rather than only deep-level
features, are extracted and fused. Additionally, we find that it will force our
model to learn more details by using the learnable upsampling method (i.e.,
transposed convolution) only on residual branch (instead of using it both on
residual branch and identity branch) while using the bicubic interpolation on
the other branch. Analytic experiments show that our method achieves
competitive results compared with the state-of-the-art methods and maintains
faster speed as well.
CNN-based Preprocessing to Optimize Watershed-based Cell Segmentation in 3D Confocal Microscopy Images
Authors: Dennis Eschweiler, Thiago V. Spina, Rohan C. Choudhury, Elliot Meyerowitz, Alexandre Cunha, Johannes Stegmaier
Date: 2018-10-16 00:00:00
Tasks: Cell Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation

The quantitative analysis of cellular membranes helps understanding
developmental processes at the cellular level. Particularly 3D microscopic
image data offers valuable insights into cell dynamics, but error-free
automatic segmentation remains challenging due to the huge amount of data
generated and strong variations in image intensities. In this paper, we propose
a new 3D segmentation approach which combines the discriminative power of
convolutional neural networks (CNNs) for preprocessing and investigates the
performance of three watershed-based postprocessing strategies (WS), which are
well suited to segment object shapes, even when supplied with vague seed and
boundary constraints. To leverage the full potential of the watershed
algorithm, the multi-instance segmentation problem is initially interpreted as
three-class semantic segmentation problem, which in turn is well-suited for the
application of CNNs. Using manually annotated 3D confocal microscopy images of
Arabidopsis thaliana, we show the superior performance of the proposed method
compared to the state of the art.
TNE: A Latent Model for Representation Learning on Networks
Authors: Abdulkadir Çelikkanat, Fragkiskos D. Malliaros
Date: 2018-10-16 00:00:00
Tasks: Community Detection, Link Prediction, Node Classification, Representation Learning, Word Embeddings

Network representation learning (NRL) methods aim to map each vertex into a
low dimensional space by preserving the local and global structure of a given
network, and in recent years they have received a significant attention thanks
to their success in several challenging problems. Although various approaches
have been proposed to compute node embeddings, many successful methods benefit
from random walks in order to transform a given network into a collection of
sequences of nodes and then they target to learn the representation of nodes by
predicting the context of each vertex within the sequence. In this paper, we
introduce a general framework to enhance the embeddings of nodes acquired by
means of the random walk-based approaches. Similar to the notion of topical
word embeddings in NLP, the proposed method assigns each vertex to a topic with
the favor of various statistical models and community detection methods, and
then generates the enhanced community representations. We evaluate our method
on two downstream tasks: node classification and link prediction. The
experimental results demonstrate that the incorporation of vertex and topic
embeddings outperform widely-known baseline NRL methods.
Neural Morphological Tagging for Estonian
Authors: Alexander Tkachenko, Kairit Sirts
Date: 2018-10-16 00:00:00
Tasks: Morphological Disambiguation, Morphological Tagging, TAG

We develop neural morphological tagging and disambiguation models for
Estonian. First, we experiment with two neural architectures for morphological
tagging - a standard multiclass classifier which treats each morphological tag
as a single unit, and a sequence model which handles the morphological tags as
sequences of morphological category values. Secondly, we complement these
models with the analyses generated by a rule-based Estonian morphological
analyser (MA) VABAMORF , thus performing a soft morphological disambiguation.
We compare two ways of supplementing a neural morphological tagger with the MA
outputs: firstly, by adding the combined analyses embeddings to the word
representation input to the neural tagging model, and secondly, by adopting an
attention mechanism to focus on the most relevant analyses generated by the MA.
Experiments on three Estonian datasets show that our neural architectures
consistently outperform the non-neural baselines, including HMM-disambiguated
VABAMORF, while augmenting models with MA outputs results in a further
performance boost for both models.
Creating a New Persian Poet Based on Machine Learning
Authors: Mehdi Hosseini Moghadam, Bardia Panahbehagh
Date: 2018-10-16 00:00:00
Tasks: BIG-bench Machine Learning

In this article we describe an application of Machine Learning (ML) and
Linguistic Modeling to generate persian poems. In fact we teach machine by
reading and learning persian poems to generate fake poems in the same style of
the original poems. As two well known poets we used Hafez (1310-1390) and Saadi
(1210-1292) poems. First we feed the machine with Hafez poems to generate fake
poems with the same style and then we feed the machine with the both Hafez and
Saadi poems to generate a new style poems which is combination of these two
poets styles with emotional (Hafez) and rational (Saadi) elements. This idea of
combination of different styles with ML opens new gates for extending the
treasure of past literature of different cultures. Results show with enough
memory, processing power and time it is possible to generate reasonable good
poems.