pwc-archive/papers-with-abstracts huggingface dataset:
Cross-lingual transfer learning for spoken language understanding
Authors: Quynh Ngoc Thi Do, Judith Gaspers
Date: 2019-04-03 00:00:00
Tasks: Cross-Lingual Transfer, Spoken Language Understanding, Transfer Learning

Typically, spoken language understanding (SLU) models are trained on
annotated data which are costly to gather. Aiming to reduce data needs for
bootstrapping a SLU system for a new language, we present a simple but
effective weight transfer approach using data from another language. The
approach is evaluated with our promising multi-task SLU framework developed
towards different languages. We evaluate our approach on the ATIS and a
real-world SLU dataset, showing that i) our monolingual models outperform the
state-of-the-art, ii) we can reduce data amounts needed for bootstrapping a SLU
system for a new language greatly, and iii) while multitask training improves
over separate training, different weight transfer settings may work best for
different SLU modules.
Fourier Phase Retrieval with Extended Support Estimation via Deep Neural Network
Authors: Kyung-Su Kim, Sae-Young Chung
Date: 2019-04-03 00:00:00
Tasks: Retrieval

We consider the problem of sparse phase retrieval from Fourier transform magnitudes to recover the $k$-sparse signal vector and its support $\mathcal{T}$. We exploit extended support estimate $\mathcal{E}$ with size larger than $k$ satisfying $\mathcal{E} \supseteq \mathcal{T}$ and obtained by a trained deep neural network (DNN). To make the DNN learnable, it provides $\mathcal{E}$ as the union of equivalent solutions of $\mathcal{T}$ by utilizing modulo Fourier invariances. Set $\mathcal{E}$ can be estimated with short running time via the DNN, and support $\mathcal{T}$ can be determined from the DNN output rather than from the full index set by applying hard thresholding to $\mathcal{E}$. Thus, the DNN-based extended support estimation improves the reconstruction performance of the signal with a low complexity burden dependent on $k$. Numerical results verify that the proposed scheme has a superior performance with lower complexity compared to local search-based greedy sparse phase retrieval and a state-of-the-art variant of the Fienup method.
Deep Neural Networks for Rotation-Invariance Approximation and Learning
Authors: Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
Date: 2019-04-03 00:00:00

Based on the tree architecture, the objective of this paper is to design deep
neural networks with two or more hidden layers (called deep nets) for
realization of radial functions so as to enable rotational invariance for
near-optimal function approximation in an arbitrarily high dimensional
Euclidian space. It is shown that deep nets have much better performance than
shallow nets (with only one hidden layer) in terms of approximation accuracy
and learning capabilities. In particular, for learning radial functions, it is
shown that near-optimal rate can be achieved by deep nets but not by shallow
nets. Our results illustrate the necessity of depth in neural network design
for realization of rotation-invariance target functions.
Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer
Authors: Edward Beeching, Christian Wolf, Jilles Dibangoye, Olivier Simonin
Date: 2019-04-03 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL), Scene Understanding

An important goal of research in Deep Reinforcement Learning in mobile
robotics is to train agents capable of solving complex tasks, which require a
high level of scene understanding and reasoning from an egocentric perspective.
When trained from simulations, optimal environments should satisfy a currently
unobtainable combination of high-fidelity photographic observations, massive
amounts of different environment configurations and fast simulation speeds. In
this paper we argue that research on training agents capable of complex
reasoning can be simplified by decoupling from the requirement of high fidelity
photographic observations. We present a suite of tasks requiring complex
reasoning and exploration in continuous, partially observable 3D environments.
The objective is to provide challenging scenarios and a robust baseline agent
architecture that can be trained on mid-range consumer hardware in under 24h.
Our scenarios combine two key advantages: (i) they are based on a simple but
highly efficient 3D environment (ViZDoom) which allows high speed simulation
(12000fps); (ii) the scenarios provide the user with a range of difficulty
settings, in order to identify the limitations of current state of the art
algorithms and network architectures. We aim to increase accessibility to the
field of Deep-RL by providing baselines for challenging scenarios where new
ideas can be iterated on quickly. We argue that the community should be able to
address challenging problems in reasoning of mobile agents without the need for
a large compute infrastructure.
GFF: Gated Fully Fusion for Semantic Segmentation
Authors: Xiangtai Li, Houlong Zhao, Lei Han, Yunhai Tong, Kuiyuan Yang
Date: 2019-04-03 00:00:00
Tasks: Scene Parsing, Scene Understanding, Segmentation, Semantic Segmentation

Semantic segmentation generates comprehensive understanding of scenes through densely predicting the category for each pixel. High-level features from Deep Convolutional Neural Networks already demonstrate their effectiveness in semantic segmentation tasks, however the coarse resolution of high-level features often leads to inferior results for small/thin objects where detailed information is important. It is natural to consider importing low level features to compensate for the lost detailed information in high-level features.Unfortunately, simply combining multi-level features suffers from the semantic gap among them. In this paper, we propose a new architecture, named Gated Fully Fusion (GFF), to selectively fuse features from multiple levels using gates in a fully connected way. Specifically, features at each level are enhanced by higher-level features with stronger semantics and lower-level features with more details, and gates are used to control the propagation of useful information which significantly reduces the noises during fusion. We achieve the state of the art results on four challenging scene parsing datasets including Cityscapes, Pascal Context, COCO-stuff and ADE20K.
Correlation Congruence for Knowledge Distillation
Authors: Baoyun Peng, Xiao Jin, Jiaheng Liu, Shunfeng Zhou, Yi-Chao Wu, Yu Liu, Dongsheng Li, Zhaoning Zhang
Date: 2019-04-03 00:00:00
Proceeding: ICCV 2019 10
Tasks: Face Recognition, image-classification, Image Classification, Knowledge Distillation, Metric Learning, Transfer Learning

Most teacher-student frameworks based on knowledge distillation (KD) depend
on a strong congruent constraint on instance level. However, they usually
ignore the correlation between multiple instances, which is also valuable for
knowledge transfer. In this work, we propose a new framework named correlation
congruence for knowledge distillation (CCKD), which transfers not only the
instance-level information, but also the correlation between instances.
Furthermore, a generalized kernel method based on Taylor series expansion is
proposed to better capture the correlation between instances. Empirical
experiments and ablation studies on image classification tasks (including
CIFAR-100, ImageNet-1K) and metric learning tasks (including ReID and Face
Recognition) show that the proposed CCKD substantially outperforms the original
KD and achieves state-of-the-art accuracy compared with other SOTA KD-based
methods. The CCKD can be easily deployed in the majority of the teacher-student
framework such as KD and hint-based learning methods.
MAVNet: an Effective Semantic Segmentation Micro-Network for MAV-based Tasks
Authors: Ty Nguyen, Shreyas S. Shivakumar, Ian D. Miller, James Keller, Elijah S. Lee, Alex Zhou, Tolga Ozaslan, Giuseppe Loianno, Joseph H. Harwood, Jennifer Wozencraft, Camillo J. Taylor, Vijay Kumar
Date: 2019-04-03 00:00:00
Tasks: Image Segmentation, Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation, Visual Odometry

Real-time semantic image segmentation on platforms subject to size, weight and power (SWaP) constraints is a key area of interest for air surveillance and inspection. In this work, we propose MAVNet: a small, light-weight, deep neural network for real-time semantic segmentation on micro Aerial Vehicles (MAVs). MAVNet, inspired by ERFNet, features 400 times fewer parameters and achieves comparable performance with some reference models in empirical experiments. Our model achieves a trade-off between speed and accuracy, achieving up to 48 FPS on an NVIDIA 1080Ti and 9 FPS on the NVIDIA Jetson Xavier when processing high resolution imagery. Additionally, we provide two novel datasets that represent challenges in semantic segmentation for real-time MAV tracking and infrastructure inspection tasks and verify MAVNet on these datasets. Our algorithm and datasets are made publicly available.
Preference-Informed Fairness
Authors: Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona
Date: 2019-04-03 00:00:00
Tasks: Decision Making, Fairness

We study notions of fairness in decision-making systems when individuals have diverse preferences over the possible outcomes of the decisions. Our starting point is the seminal work of Dwork et al. which introduced a notion of individual fairness (IF): given a task-specific similarity metric, every pair of individuals who are similarly qualified according to the metric should receive similar outcomes. We show that when individuals have diverse preferences over outcomes, requiring IF may unintentionally lead to less-preferred outcomes for the very individuals that IF aims to protect. A natural alternative to IF is the classic notion of fair division, envy-freeness (EF): no individual should prefer another individual's outcome over their own. Although EF allows for solutions where all individuals receive a highly-preferred outcome, EF may also be overly-restrictive. For instance, if many individuals agree on the best outcome, then if any individual receives this outcome, they all must receive it, regardless of each individual's underlying qualifications for the outcome. We introduce and study a new notion of preference-informed individual fairness (PIIF) that is a relaxation of both individual fairness and envy-freeness. At a high-level, PIIF requires that outcomes satisfy IF-style constraints, but allows for deviations provided they are in line with individuals' preferences. We show that PIIF can permit outcomes that are more favorable to individuals than any IF solution, while providing considerably more flexibility to the decision-maker than EF. In addition, we show how to efficiently optimize any convex objective over the outcomes subject to PIIF for a rich class of individual preferences. Finally, we demonstrate the broad applicability of the PIIF framework by extending our definitions and algorithms to the multiple-task targeted advertising setting introduced by Dwork and Ilvento.
Random Projection in Neural Episodic Control
Authors: Daichi Nishio, Satoshi Yamane
Date: 2019-04-03 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

End-to-end deep reinforcement learning has enabled agents to learn with
little preprocessing by humans. However, it is still difficult to learn stably
and efficiently because the learning method usually uses a nonlinear function
approximation. Neural Episodic Control (NEC), which has been proposed in order
to improve sample efficiency, is able to learn stably by estimating action
values using a non-parametric method. In this paper, we propose an architecture
that incorporates random projection into NEC to train with more stability. In
addition, we verify the effectiveness of our architecture by Atari's five
games. The main idea is to reduce the number of parameters that have to learn
by replacing neural networks with random projection in order to reduce
dimensions while keeping the learning end-to-end.
Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning
Authors: Shichen Liu, Tianye Li, Weikai Chen, Hao Li
Date: 2019-04-03 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Object Reconstruction, Single-View 3D Reconstruction

Rendering bridges the gap between 2D vision and 3D scenes by simulating the
physical process of image formation. By inverting such renderer, one can think
of a learning approach to infer 3D information from 2D images. However,
standard graphics renderers involve a fundamental discretization step called
rasterization, which prevents the rendering process to be differentiable, hence
able to be learned. Unlike the state-of-the-art differentiable renderers, which
only approximate the rendering gradient in the back propagation, we propose a
truly differentiable rendering framework that is able to (1) directly render
colorized mesh using differentiable functions and (2) back-propagate efficient
supervision signals to mesh vertices and their attributes from various forms of
image representations, including silhouette, shading and color images. The key
to our framework is a novel formulation that views rendering as an aggregation
function that fuses the probabilistic contributions of all mesh triangles with
respect to the rendered pixels. Such formulation enables our framework to flow
gradients to the occluded and far-range vertices, which cannot be achieved by
the previous state-of-the-arts. We show that by using the proposed renderer,
one can achieve significant improvement in 3D unsupervised single-view
reconstruction both qualitatively and quantitatively. Experiments also
demonstrate that our approach is able to handle the challenging tasks in
image-based shape fitting, which remain nontrivial to existing differentiable
renderers.
Patchwork: A Patch-wise Attention Network for Efficient Object Detection and Segmentation in Video Streams
Authors: Yuning Chai
Date: 2019-04-03 00:00:00
Proceeding: ICCV 2019 10
Tasks: Hard Attention, object-detection, Object Detection, Q-Learning, Semantic Segmentation, Video Object Detection, Video Object Segmentation, Video Semantic Segmentation

Recent advances in single-frame object detection and segmentation techniques have motivated a wide range of works to extend these methods to process video streams. In this paper, we explore the idea of hard attention aimed for latency-sensitive applications. Instead of reasoning about every frame separately, our method selects and only processes a small sub-window of the frame. Our technique then makes predictions for the full frame based on the sub-windows from previous frames and the update from the current sub-window. The latency reduction by this hard attention mechanism comes at the cost of degraded accuracy. We made two contributions to address this. First, we propose a specialized memory cell that recovers lost context when processing sub-windows. Secondly, we adopt a Q-learning-based policy training strategy that enables our approach to intelligently select the sub-windows such that the staleness in the memory hurts the performance the least. Our experiments suggest that our approach reduces the latency by approximately four times without significantly sacrificing the accuracy on the ImageNet VID video object detection dataset and the DAVIS video object segmentation dataset. We further demonstrate that we can reinvest the saved computation into other parts of the network, and thus resulting in an accuracy increase at a comparable computational cost as the original system and beating other recently proposed state-of-the-art methods in the low latency range.
Multi-task Learning for Chinese Word Usage Errors Detection
Authors: Jinbin Zhang, Heng Wang
Date: 2019-04-03 00:00:00
Tasks: Multi-Task Learning, POS, POS Tagging, Prediction

Chinese word usage errors often occur in non-native Chinese learners'
writing. It is very helpful for non-native Chinese learners to detect them
automatically when learning writing. In this paper, we propose a novel
approach, which takes advantages of different auxiliary tasks, such as
POS-tagging prediction and word log frequency prediction, to help the task of
Chinese word usage error detection. With the help of these auxiliary tasks, we
achieve the state-of-the-art results on the performances on the HSK corpus
data, without any other extra data.
Recognition of Advertisement Emotions with Application to Computational Advertising
Authors: Abhinav Shukla, Shruti Shriya Gullapuram, Harish Katti, Mohan Kankanhalli, Stefan Winkler, Ramanathan Subramanian
Date: 2019-04-03 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Multi-Task Learning

Advertisements (ads) often contain strong affective content to capture viewer
attention and convey an effective message to the audience. However, most
computational affect recognition (AR) approaches examine ads via the text
modality, and only limited work has been devoted to decoding ad emotions from
audiovisual or user cues. This work (1) compiles an affective ad dataset
capable of evoking coherent emotions across users; (2) explores the efficacy of
content-centric convolutional neural network (CNN) features for AR vis-\~a-vis
handcrafted audio-visual descriptors; (3) examines user-centric ad AR from
Electroencephalogram (EEG) responses acquired during ad-viewing, and (4)
demonstrates how better affect predictions facilitate effective computational
advertising as determined by a study involving 18 users. Experiments reveal
that (a) CNN features outperform audiovisual descriptors for content-centric
AR; (b) EEG features are able to encode ad-induced emotions better than
content-based features; (c) Multi-task learning performs best among a slew of
classification algorithms to achieve optimal AR, and (d) Pursuant to (b), EEG
features also enable optimized ad insertion onto streamed video, as compared to
content-based or manual insertion techniques in terms of ad memorability and
overall user experience.
Defeats GAN: A Simpler Model Outperforms in Knowledge Representation Learning
Authors: Heng Wang, Mingzhi Mao
Date: 2019-04-03 00:00:00
Tasks: Link Prediction, Representation Learning

The goal of knowledge representation learning is to embed entities and
relations into a low-dimensional, continuous vector space. How to push a model
to its limit and obtain better results is of great significance in knowledge
graph's applications. We propose a simple and elegant method, Trans-DLR, whose
main idea is dynamic learning rate control during training. Our method achieves
remarkable improvement, compared with recent GAN-based method. Moreover, we
introduce a new negative sampling trick which corrupts not only entities, but
also relations, in different probabilities. We also develop an efficient way,
which fully utilizes multiprocessing and parallel computing, to speed up
evaluation of the model in link prediction tasks. Experiments show that our
method is effective.
Multimodal Representation Learning using Deep Multiset Canonical Correlation
Authors: Krishna Somandepalli, Naveen Kumar, Ruchir Travadi, Shrikanth Narayanan
Date: 2019-04-03 00:00:00
Tasks: Representation Learning

We propose Deep Multiset Canonical Correlation Analysis (dMCCA) as an
extension to representation learning using CCA when the underlying signal is
observed across multiple (more than two) modalities. We use deep learning
framework to learn non-linear transformations from different modalities to a
shared subspace such that the representations maximize the ratio of between-
and within-modality covariance of the observations. Unlike linear discriminant
analysis, we do not need class information to learn these representations, and
we show that this model can be trained for complex data using mini-batches.
Using synthetic data experiments, we show that dMCCA can effectively recover
the common signal across the different modalities corrupted by multiplicative
and additive noise. We also analyze the sensitivity of our model to recover the
correlated components with respect to mini-batch size and dimension of the
embeddings. Performance evaluation on noisy handwritten datasets shows that our
model outperforms other CCA-based approaches and is comparable to deep neural
network models trained end-to-end on this dataset.
Image Generation From Small Datasets via Batch Statistics Adaptation
Authors: Atsuhiro Noguchi, Tatsuya Harada
Date: 2019-04-03 00:00:00
Proceeding: ICCV 2019 10
Tasks: Common Sense Reasoning, Diversity, Image Generation

Thanks to the recent development of deep generative models, it is becoming possible to generate high-quality images with both fidelity and diversity. However, the training of such generative models requires a large dataset. To reduce the amount of data required, we propose a new method for transferring prior knowledge of the pre-trained generator, which is trained with a large dataset, to a small dataset in a different domain. Using such prior knowledge, the model can generate images leveraging some common sense that cannot be acquired from a small dataset. In this work, we propose a novel method focusing on the parameters for batch statistics, scale and shift, of the hidden layers in the generator. By training only these parameters in a supervised manner, we achieved stable training of the generator, and our method can generate higher quality images compared to previous methods without collapsing, even when the dataset is small (~100). Our results show that the diversity of the filters acquired in the pre-trained generator is important for the performance on the target domain. Our method makes it possible to add a new class or domain to a pre-trained generator without disturbing the performance on the original domain.
M2KD: Multi-model and Multi-level Knowledge Distillation for Incremental Learning
Authors: Peng Zhou, Long Mai, Jianming Zhang, Ning Xu, Zuxuan Wu, Larry S. Davis
Date: 2019-04-03 00:00:00
Tasks: Incremental Learning, Knowledge Distillation

Incremental learning targets at achieving good performance on new categories without forgetting old ones. Knowledge distillation has been shown critical in preserving the performance on old classes. Conventional methods, however, sequentially distill knowledge only from the last model, leading to performance degradation on the old classes in later incremental learning steps. In this paper, we propose a multi-model and multi-level knowledge distillation strategy. Instead of sequentially distilling knowledge only from the last model, we directly leverage all previous model snapshots. In addition, we incorporate an auxiliary distillation to further preserve knowledge encoded at the intermediate feature levels. To make the model more memory efficient, we adapt mask based pruning to reconstruct all previous models with a small memory footprint. Experiments on standard incremental learning benchmarks show that our method preserves the knowledge on old classes better and improves the overall performance over standard distillation techniques.