pwc-archive/papers-with-abstracts huggingface dataset:
Pyramid Embedded Generative Adversarial Network for Automated Font Generation
Authors: Donghui Sun, Qing Zhang, Jun Yang
Date: 2018-11-20 00:00:00
Tasks: Decoder, Font Generation, Generative Adversarial Network

In this paper, we investigate the Chinese font synthesis problem and propose
a Pyramid Embedded Generative Adversarial Network (PEGAN) to automatically
generate Chinese character images. The PEGAN consists of one generator and one
discriminator. The generator is built using one encoder-decoder structure with
cascaded refinement connections and mirror skip connections. The cascaded
refinement connections embed a multiscale pyramid of downsampled original input
into the encoder feature maps of different layers, and multi-scale feature maps
from the encoder are connected to the corresponding feature maps in the decoder
to make the mirror skip connections. Through combining the generative
adversarial loss, pixel-wise loss, category loss and perceptual loss, the
generator and discriminator can be trained alternately to synthesize character
images. In order to verify the effectiveness of our proposed PEGAN, we first
build one evaluation set, in which the characters are selected according to
their stroke number and frequency of use, and then use both qualitative and
quantitative metrics to measure the performance of our model comparing with the
baseline method. The experimental results demonstrate the effectiveness of our
proposed model, it shows the potential to automatically extend small font banks
into complete ones.
Bi-Adversarial Auto-Encoder for Zero-Shot Learning
Authors: Yunlong Yu, Zhong Ji, Yanwei Pang, Jichang Guo, Zhongfei Zhang, Fei Wu
Date: 2018-11-20 00:00:00
Tasks: Decoder, Zero-Shot Learning

Existing generative Zero-Shot Learning (ZSL) methods only consider the
unidirectional alignment from the class semantics to the visual features while
ignoring the alignment from the visual features to the class semantics, which
fails to construct the visual-semantic interactions well. In this paper, we
propose to synthesize visual features based on an auto-encoder framework paired
with bi-adversarial networks respectively for visual and semantic modalities to
reinforce the visual-semantic interactions with a bi-directional alignment,
which ensures the synthesized visual features to fit the real visual
distribution and to be highly related to the semantics. The encoder aims at
synthesizing real-like visual features while the decoder forces both the real
and the synthesized visual features to be more related to the class semantics.
To further capture the discriminative information of the synthesized visual
features, both the real and synthesized visual features are forced to be
classified into the correct classes via a classification network. Experimental
results on four benchmark datasets show that the proposed approach is
particularly competitive on both the traditional ZSL and the generalized ZSL
tasks.
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes
Authors: Nora K. Speicher, Nico Pfeifer
Date: 2018-11-20 00:00:00
Tasks: Clustering, Dimensionality Reduction, Discovery Of Integrative Cancer Subtypes, Survival Analysis

Due to the complexity of cancer, clustering algorithms have been used to
disentangle the observed heterogeneity and identify cancer subtypes that can be
treated specifically. While kernel based clustering approaches allow the use of
more than one input matrix, which is an important factor when considering a
multidimensional disease like cancer, the clustering results remain hard to
evaluate and, in many cases, it is unclear which piece of information had which
impact on the final result. In this paper, we propose an extension of multiple
kernel learning clustering that enables the characterization of each identified
patient cluster based on the features that had the highest impact on the
result. To this end, we combine feature clustering with multiple kernel
dimensionality reduction and introduce FIPPA, a score which measures the
feature cluster impact on a patient cluster. Results: We applied the approach
to different cancer types described by four different data types with the aim
of identifying integrative patient subtypes and understanding which features
were most important for their identification. Our results show that our method
does not only have state-of-the-art performance according to standard measures
(e.g., survival analysis), but, based on the high impact features, it also
produces meaningful explanations for the molecular bases of the subtypes. This
could provide an important step in the validation of potential cancer subtypes
and enable the formulation of new hypotheses concerning individual patient
groups. Similar analysis are possible for other disease phenotypes.
Another Diversity-Promoting Objective Function for Neural Dialogue Generation
Authors: Ryo Nakamura, Katsuhito Sudoh, Koichiro Yoshino, Satoshi Nakamura
Date: 2018-11-20 00:00:00
Tasks: Dialogue Generation, Diversity

Although generation-based dialogue systems have been widely researched, the
response generations by most existing systems have very low diversities. The
most likely reason for this problem is Maximum Likelihood Estimation (MLE) with
Softmax Cross-Entropy (SCE) loss. MLE trains models to generate the most
frequent responses from enormous generation candidates, although in actual
dialogues there are various responses based on the context. In this paper, we
propose a new objective function called Inverse Token Frequency (ITF) loss,
which individually scales smaller loss for frequent token classes and larger
loss for rare token classes. This function encourages the model to generate
rare tokens rather than frequent tokens. It does not complicate the model and
its training is stable because we only replace the objective function. On the
OpenSubtitles dialogue dataset, our loss model establishes a state-of-the-art
DIST-1 of 7.56, which is the unigram diversity score, while maintaining a good
BLEU-1 score. On a Japanese Twitter replies dataset, our loss model achieves a
DIST-1 score comparable to the ground truth.
Model Learning for Look-ahead Exploration in Continuous Control
Authors: Arpit Agarwal, Katharina Muelling, Katerina Fragkiadaki
Date: 2018-11-20 00:00:00
Tasks: continuous-control, Continuous Control, Reinforcement Learning, Reinforcement Learning (RL)

We propose an exploration method that incorporates look-ahead search over
basic learnt skills and their dynamics, and use it for reinforcement learning
(RL) of manipulation policies . Our skills are multi-goal policies learned in
isolation in simpler environments using existing multigoal RL formulations,
analogous to options or macroactions. Coarse skill dynamics, i.e., the state
transition caused by a (complete) skill execution, are learnt and are unrolled
forward during lookahead search. Policy search benefits from temporal
abstraction during exploration, though itself operates over low-level primitive
actions, and thus the resulting policies does not suffer from suboptimality and
inflexibility caused by coarse skill chaining. We show that the proposed
exploration strategy results in effective learning of complex manipulation
policies faster than current state-of-the-art RL methods, and converges to
better policies than methods that use options or parametrized skills as
building blocks of the policy itself, as opposed to guiding exploration. We
show that the proposed exploration strategy results in effective learning of
complex manipulation policies faster than current state-of-the-art RL methods,
and converges to better policies than methods that use options or parameterized
skills as building blocks of the policy itself, as opposed to guiding
exploration.
Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers
Authors: Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda
Date: 2018-11-20 00:00:00
Tasks: Multiple Instance Learning, Time Series, Time Series Analysis, Time Series Classification

We propose a new formulation of Multiple-Instance Learning (MIL). In typical
MIL settings, a unit of data is given as a set of instances called a bag and
the goal is to find a good classifier of bags based on similarity from a single
or finitely many "shapelets" (or patterns), where the similarity of the bag
from a shapelet is the maximum similarity of instances in the bag. Classifiers
based on a single shapelet are not sufficiently strong for certain
applications. Additionally, previous work with multiple shapelets has
heuristically chosen some of the instances as shapelets with no theoretical
guarantee of its generalization ability. Our formulation provides a richer
class of the final classifiers based on infinitely many shapelets. We provide
an efficient algorithm for the new formulation, in addition to generalization
bound. Our empirical study demonstrates that our approach is effective not only
for MIL tasks but also for Shapelet Learning for time-series classification.
ChainGAN: A sequential approach to GANs
Authors: Safwan Hossain, Kiarash Jamali, Yuchen Li, Frank Rudzicz
Date: 2018-11-20 00:00:00
Proceeding: ICLR 2019 5

We propose a new architecture and training methodology for generative
adversarial networks. Current approaches attempt to learn the transformation
from a noise sample to a generated data sample in one shot. Our proposed
generator architecture, called $\textit{ChainGAN}$, uses a two-step process. It
first attempts to transform a noise vector into a crude sample, similar to a
traditional generator. Next, a chain of networks, called $\textit{editors}$,
attempt to sequentially enhance this sample. We train each of these units
independently, instead of with end-to-end backpropagation on the entire chain.
Our model is robust, efficient, and flexible as we can apply it to various
network architectures. We provide rationale for our choices and experimentally
evaluate our model, achieving competitive results on several datasets.
Lightweight Lipschitz Margin Training for Certified Defense against Adversarial Examples
Authors: Hajime Ono, Tsubasa Takahashi, Kazuya Kakizaki
Date: 2018-11-20 00:00:00

How can we make machine learning provably robust against adversarial examples
in a scalable way? Since certified defense methods, which ensure
$\epsilon$-robust, consume huge resources, they can only achieve small degree
of robustness in practice. Lipschitz margin training (LMT) is a scalable
certified defense, but it can also only achieve small robustness due to
over-regularization. How can we make certified defense more efficiently? We
present LC-LMT, a light weight Lipschitz margin training which solves the above
problem. Our method has the following properties; (a) efficient: it can achieve
$\epsilon$-robustness at early epoch, and (b) robust: it has a potential to get
higher robustness than LMT. In the evaluation, we demonstrate the benefits of
the proposed method. LC-LMT can achieve required robustness more than 30 epoch
earlier than LMT in MNIST, and shows more than 90 $\%$ accuracy against both
legitimate and adversarial inputs.
An empirical evaluation of AMR parsing for legal documents
Authors: Sinh Vu Trong, Minh Nguyen Le
Date: 2018-11-20 00:00:00
Tasks: Abstract Meaning Representation, AMR Parsing

Many approaches have been proposed to tackle the problem of Abstract Meaning
Representation (AMR) parsing, helps solving various natural language processing
issues recently. In our paper, we provide an overview of different methods in
AMR parsing and their performances when analyzing legal documents. We conduct
experiments of different AMR parsers on our annotated dataset extracted from
the English version of Japanese Civil Code. Our results show the limitations as
well as open a room for improvements of current parsing techniques when
applying in this complicated domain.
Scene Graph Generation via Conditional Random Fields
Authors: Weilin Cong, William Wang, Wang-Chien Lee
Date: 2018-11-20 00:00:00
Tasks: Graph Generation, Image Retrieval, Object, object-detection, Object Detection, Retrieval, Scene Graph Generation, Triplet

Despite the great success object detection and segmentation models have achieved in recognizing individual objects in images, performance on cognitive tasks such as image caption, semantic image retrieval, and visual QA is far from satisfactory. To achieve better performance on these cognitive tasks, merely recognizing individual object instances is insufficient. Instead, the interactions between object instances need to be captured in order to facilitate reasoning and understanding of the visual scenes in an image. Scene graph, a graph representation of images that captures object instances and their relationships, offers a comprehensive understanding of an image. However, existing techniques on scene graph generation fail to distinguish subjects and objects in the visual scenes of images and thus do not perform well with real-world datasets where exist ambiguous object instances. In this work, we propose a novel scene graph generation model for predicting object instances and its corresponding relationships in an image. Our model, SG-CRF, learns the sequential order of subject and object in a relationship triplet, and the semantic compatibility of object instance nodes and relationship nodes in a scene graph efficiently. Experiments empirically show that SG-CRF outperforms the state-of-the-art methods, on three different datasets, i.e., CLEVR, VRD, and Visual Genome, raising the Recall@100 from 24.99% to 49.95%, from 41.92% to 50.47%, and from 54.69% to 54.77%, respectively.
Factorized Distillation: Training Holistic Person Re-identification Model by Distilling an Ensemble of Partial ReID Models
Authors: Pengyuan Ren, Jianmin Li
Date: 2018-11-20 00:00:00
Tasks: Knowledge Distillation, Person Re-Identification, Retrieval

Person re-identification (ReID) is aimed at identifying the same person
across videos captured from different cameras. In the view that networks
extracting global features using ordinary network architectures are difficult
to extract local features due to their weak attention mechanisms, researchers
have proposed a lot of elaborately designed ReID networks, while greatly
improving the accuracy, the model size and the feature extraction latency are
also soaring. We argue that a relatively compact ordinary network extracting
globally pooled features has the capability to extract discriminative local
features and can achieve state-of-the-art precision if only the model's
parameters are properly learnt. In order to reduce the difficulty in learning
hard identity labels, we propose a novel knowledge distillation method:
Factorized Distillation, which factorizes both feature maps and retrieval
features of holistic ReID network to mimic representations of multiple partial
ReID models, thus transferring the knowledge from partial ReID models to the
holistic network. Experiments show that the performance of model trained with
the proposed method can outperform state-of-the-art with relatively few network
parameters.
Representation Learning of Pedestrian Trajectories Using Actor-Critic Sequence-to-Sequence Autoencoder
Authors: Ka-Ho Chow, Anish Hiranandani, Yifeng Zhang, S. -H. Gary Chan
Date: 2018-11-20 00:00:00
Tasks: Reinforcement Learning, Representation Learning

Representation learning of pedestrian trajectories transforms variable-length
timestamp-coordinate tuples of a trajectory into a fixed-length vector
representation that summarizes spatiotemporal characteristics. It is a crucial
technique to connect feature-based data mining with trajectory data. Trajectory
representation is a challenging problem, because both environmental constraints
(e.g., wall partitions) and temporal user dynamics should be meticulously
considered and accounted for. Furthermore, traditional sequence-to-sequence
autoencoders using maximum log-likelihood often require dataset covering all
the possible spatiotemporal characteristics to perform well. This is infeasible
or impractical in reality. We propose TREP, a practical pedestrian trajectory
representation learning algorithm which captures the environmental constraints
and the pedestrian dynamics without the need of any training dataset. By
formulating a sequence-to-sequence autoencoder with a spatial-aware objective
function under the paradigm of actor-critic reinforcement learning, TREP
intelligently encodes spatiotemporal characteristics of trajectories with the
capability of handling diverse trajectory patterns. Extensive experiments on
both synthetic and real datasets validate the high fidelity of TREP to
represent trajectories.
Reinforcement Learning of Active Vision for Manipulating Objects under Occlusions
Authors: Ricson Cheng, Arpit Agarwal, Katerina Fragkiadaki
Date: 2018-11-20 00:00:00
Tasks: Object, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider artificial agents that learn to jointly control their gripperand
camera in order to reinforcement learn manipulation policies in the presenceof
occlusions from distractor objects. Distractors often occlude the object of
in-terest and cause it to disappear from the field of view. We propose hand/eye
con-trollers that learn to move the camera to keep the object within the field
of viewand visible, in coordination to manipulating it to achieve the desired
goal, e.g.,pushing it to a target location. We incorporate structural biases of
object-centricattention within our actor-critic architectures, which our
experiments suggest tobe a key for good performance. Our results further
highlight the importance ofcurriculum with regards to environment difficulty.
The resulting active vision /manipulation policies outperform static camera
setups for a variety of clutteredenvironments.
Learning Robust Heterogeneous Signal Features from Parallel Neural Network for Audio Sentiment Analysis
Authors: Feiyang Chen, Ziqian Luo
Date: 2018-11-20 00:00:00
Tasks: Sentiment Analysis

Audio Sentiment Analysis is a popular research area which extends the conventional text-based sentiment analysis to depend on the effectiveness of acoustic features extracted from speech. However, current progress on audio sentiment analysis mainly focuses on extracting homogeneous acoustic features or doesn't fuse heterogeneous features effectively. In this paper, we propose an utterance-based deep neural network model, which has a parallel combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based network, to obtain representative features termed Audio Sentiment Vector (ASV), that can maximally reflect sentiment information in an audio. Specifically, our model is trained by utterance-level labels and ASV can be extracted and fused creatively from two branches. In the CNN model branch, spectrum graphs produced by signals are fed as inputs while in the LSTM model branch, inputs include spectral features and cepstrum coefficient extracted from dependent utterances in audio. Besides, Bidirectional Long Short-Term Memory (BiLSTM) with attention mechanism is used for feature fusion. Extensive experiments have been conducted to show our model can recognize audio sentiment precisely and quickly, and demonstrate our ASV is better than traditional acoustic features or vectors extracted from other deep learning models. Furthermore, experimental results indicate that the proposed model outperforms the state-of-the-art approach by 9.33\% on Multimodal Opinion-level Sentiment Intensity dataset (MOSI) dataset.
Visual Localization Under Appearance Change: Filtering Approaches
Authors: Anh-Dzung Doan, Yasir Latif, Tat-Jun Chin, Yu Liu, Shin-Fang Ch'ng, Thanh-Toan Do, Ian Reid
Date: 2018-11-20 00:00:00
Tasks: Visual Localization, Visual Place Recognition

A major focus of current research on place recognition is visual localization for autonomous driving. In this scenario, as cameras will be operating continuously, it is realistic to expect videos as an input to visual localization algorithms, as opposed to the single-image querying approach used in other visual localization works. In this paper, we show that exploiting temporal continuity in the testing sequence significantly improves visual localization - qualitatively and quantitatively. Although intuitive, this idea has not been fully explored in recent works. To this end, we propose two filtering approaches to exploit the temporal smoothness of image sequences: i) filtering on discrete domain with Hidden Markov Model, and ii) filtering on continuous domain with Monte Carlo-based visual localization. Our approaches rely on local features with an encoding technique to represent an image as a single vector. The experimental results on synthetic and real datasets show that our proposed methods achieve better results than state of the art (i.e., deep learning-based pose regression approaches) for the task on visual localization under significant appearance change. Our synthetic dataset and source code are made publicly available at https://sites.google.com/view/g2d-software/home and https://github.com/dadung/Visual-Localization-Filtering.
Gradient-Coherent Strong Regularization for Deep Neural Networks
Authors: Dae Hoon Park, Chiu Man Ho, Yi Chang, Huaqing Zhang
Date: 2018-11-20 00:00:00
Tasks: L2 Regularization

Regularization plays an important role in generalization of deep neural networks, which are often prone to overfitting with their numerous parameters. L1 and L2 regularizers are common regularization tools in machine learning with their simplicity and effectiveness. However, we observe that imposing strong L1 or L2 regularization with stochastic gradient descent on deep neural networks easily fails, which limits the generalization ability of the underlying neural networks. To understand this phenomenon, we first investigate how and why learning fails when strong regularization is imposed on deep neural networks. We then propose a novel method, gradient-coherent strong regularization, which imposes regularization only when the gradients are kept coherent in the presence of strong regularization. Experiments are performed with multiple deep architectures on three benchmark data sets for image recognition. Experimental results show that our proposed approach indeed endures strong regularization and significantly improves both accuracy and compression (up to 9.9x), which could not be achieved otherwise.