pwc-archive/papers-with-abstracts huggingface dataset:
Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation
Authors: Jiwoon Ahn, Suha Kwak
Date: 2018-03-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

The deficiency of segmentation labels is one of the main obstacles to
semantic segmentation in the wild. To alleviate this issue, we present a novel
framework that generates segmentation labels of images given their image-level
class labels. In this weakly supervised setting, trained models have been known
to segment local discriminative parts rather than the entire object area. Our
solution is to propagate such local responses to nearby areas which belong to
the same semantic entity. To this end, we propose a Deep Neural Network (DNN)
called AffinityNet that predicts semantic affinity between a pair of adjacent
image coordinates. The semantic propagation is then realized by random walk
with the affinities predicted by AffinityNet. More importantly, the supervision
employed to train AffinityNet is given by the initial discriminative part
segmentation, which is incomplete as a segmentation annotation but sufficient
for learning semantic affinities within small image areas. Thus the entire
framework relies only on image-level class labels and does not require any
extra data or annotations. On the PASCAL VOC 2012 dataset, a DNN learned with
segmentation labels generated by our method outperforms previous models trained
with the same level of supervision, and is even as competitive as those relying
on stronger supervision.
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
Authors: Xintao Wang, Ke Yu, Chao Dong, Chen Change Loy
Date: 2018-04-09 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Super-Resolution, Semantic Segmentation, Super-Resolution

Despite that convolutional neural networks (CNN) have recently demonstrated
high-quality reconstruction for single-image super-resolution (SR), recovering
natural and realistic texture remains a challenging problem. In this paper, we
show that it is possible to recover textures faithful to semantic classes. In
particular, we only need to modulate features of a few intermediate layers in a
single network conditioned on semantic segmentation probability maps. This is
made possible through a novel Spatial Feature Transform (SFT) layer that
generates affine transformation parameters for spatial-wise feature modulation.
SFT layers can be trained end-to-end together with the SR network using the
same loss function. During testing, it accepts an input image of arbitrary size
and generates a high-resolution image with just a single forward pass
conditioned on the categorical priors. Our final results show that an SR
network equipped with SFT can generate more realistic and visually pleasing
textures in comparison to state-of-the-art SRGAN and EnhanceNet.
Affective Recommendation System for Tourists by Using Emotion Generating Calculations
Authors: Takumi Ichimura, Issei Tachibana
Date: 2018-04-09 00:00:00

An emotion orientated intelligent interface consists of Emotion Generating
Calculations (EGC) and Mental State Transition Network (MSTN). We have
developed the Android EGC application software which the agent works to
evaluate the feelings in the conversation. In this paper, we develop the
tourist information system which can estimate the user's feelings at the
sightseeing spot. The system can recommend the sightseeing spot and the local
food corresponded to the user's feeling. The system calculates the
recommendation list by the estimate function which consists of Google search
results, the important degree of a term at the sightseeing website, and the the
aroused emotion by EGC. In order to show the effectiveness, this paper
describes the experimental results for some situations during Hiroshima
sightseeing.
An Adaptive Learning Method of Personality Trait Based Mood in Mental State Transition Network by Recurrent Neural Network
Authors: Takumi Ichimura, Kosuke Tanabe, Toshiyuki Yamashita
Date: 2018-04-09 00:00:00

Mental State Transition Network (MSTN) is a basic concept of approximating to
human psychological and mental responses. A stimulus calculated by Emotion
Generating Calculations (EGC) method can cause the transition of mood from an
emotional state to others. In this paper, the agent can interact with human to
realize smooth communication by an adaptive learning method of the user's
personality trait based mood. The learning method consists of the profit
sharing (PS) method and the recurrent neural network (RNN). An emotion for
sensor inputs to MSTN is calculated by EGC and the variance of emotion leads to
the change of mental state, and then the sequence of states forms an episode.
In order to learn the tendency of personality trait effectively, the
ineffective rules should be removed from the episode. PS method finds out a
detour in episode and should be deleted. Furthermore, RNN works to realize the
variance of user's mood. Some experimental results were shown the success of
representing a various human's delicate emotion.
Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations
Authors: Ju-chieh Chou, Cheng-chieh Yeh, Hung-Yi Lee, Lin-shan Lee
Date: 2018-04-09 00:00:00
Tasks: Decoder, Voice Conversion

Recently, cycle-consistent adversarial network (Cycle-GAN) has been
successfully applied to voice conversion to a different speaker without
parallel data, although in those approaches an individual model is needed for
each target speaker. In this paper, we propose an adversarial learning
framework for voice conversion, with which a single model can be trained to
convert the voice to many different speakers, all without parallel data, by
separating the speaker characteristics from the linguistic content in speech
signals. An autoencoder is first trained to extract speaker-independent latent
representations and speaker embedding separately using another auxiliary
speaker classifier to regularize the latent representation. The decoder then
takes the speaker-independent latent representation and the target speaker
embedding as the input to generate the voice of the target speaker with the
linguistic content of the source utterance. The quality of decoder output is
further improved by patching with the residual signal produced by another pair
of generator and discriminator. A target speaker set size of 20 was tested in
the preliminary experiments, and very good voice quality was obtained.
Conventional voice conversion metrics are reported. We also show that the
speaker information has been properly reduced from the latent representations.
A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction
Authors: Mingxing Duan, Kenli Li, Qi Tian
Date: 2018-04-09 00:00:00
Tasks: Attribute, Multi-Task Learning

Face multi-attribute prediction benefits substantially from multi-task
learning (MTL), which learns multiple face attributes simultaneously to achieve
shared or mutually related representations of different attributes. The most
widely used MTL convolutional neural network is heuristically or empirically
designed by sharing all of the convolutional layers and splitting at the fully
connected layers for task-specific losses. However, it is improper to view all
low and mid-level features for different attributes as being the same,
especially when these attributes are only loosely related. In this paper, we
propose a novel multi-attribute tensor correlation neural network (MTCN) for
face attribute prediction. The structure shares the information in low-level
features (e.g., the first two convolutional layers) but splits that in
high-level features (e.g., from the third convolutional layer to the fully
connected layer). At the same time, during high-level feature extraction, each
subnetwork (e.g., Age-Net, Gender-Net, ..., and Smile-Net) excavates closely
related features from other networks to enhance its features. Then, we project
the features of the C9 layers of the fine-tuned subnetworks into a highly
correlated space by using a novel tensor correlation analysis algorithm
(NTCCA). The final face attribute prediction is made based on the correlation
matrix. Experimental results on benchmarks with multiple face attributes
(CelebA and LFWA) show that the proposed approach has superior performance
compared to state-of-the-art methods.
Cognition in Dynamical Systems, Second Edition
Authors: Jack Hall
Date: 2018-04-09 00:00:00

Cognition is the process of knowing. As carried out by a dynamical system, it is the process by which the system absorbs information into its state. A complex network of agents cognizes knowledge about its environment, internal dynamics and initial state by forming emergent, macro-level patterns. Such patterns require each agent to find its place while partially aware of the whole pattern. Such partial awareness can be achieved by separating the system dynamics into two parts by timescale: the propagation dynamics and the pattern dynamics. The fast propagation dynamics describe the spread of signals across the network. If they converge to a fixed point for any quasi-static state of the slow pattern dynamics, that fixed point represents an aggregate of macro-level information. On longer timescales, agents coordinate via positive feedback to form patterns, which are defined using closed walks in the graph of agents. Patterns can be coherent, in that every part of the pattern depends on every other part for context. Coherent patterns are acausal, in that (a) they cannot be predicted and (b) no part of the stored knowledge can be mapped to any part of the pattern, or vice versa. A cognitive network's knowledge is encoded or embodied by the selection of patterns which emerge. The theory of cognition summarized here can model autocatalytic reaction-diffusion systems, artificial neural networks, market economies and ant colony optimization, among many other real and virtual systems. This theory suggests a new understanding of complexity as a lattice of contexts rather than a single measure.
Latent Space Policies for Hierarchical Reinforcement Learning
Authors: Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, Sergey Levine
Date: 2018-04-09 00:00:00
Proceeding: ICML 2018 7
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We address the problem of learning hierarchical deep neural network policies
for reinforcement learning. In contrast to methods that explicitly restrict or
cripple lower layers of a hierarchy to force them to use higher-level
modulating signals, each layer in our framework is trained to directly solve
the task, but acquires a range of diverse strategies via a maximum entropy
reinforcement learning objective. Each layer is also augmented with latent
random variables, which are sampled from a prior distribution during the
training of that layer. The maximum entropy objective causes these latent
variables to be incorporated into the layer's policy, and the higher level
layer can directly control the behavior of the lower layer through this latent
space. Furthermore, by constraining the mapping from latent variables to
actions to be invertible, higher layers retain full expressivity: neither the
higher layers nor the lower layers are constrained in their behavior. Our
experimental evaluation demonstrates that we can improve on the performance of
single-layer policies on standard benchmark tasks simply by adding additional
layers, and that our method can solve more complex sparse-reward tasks by
learning higher-level policies on top of high-entropy skills optimized for
simple low-level objectives.
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation
Authors: Lei Tai, Jingwei Zhang, Ming Liu, Joschka Boedecker, Wolfram Burgard
Date: 2016-12-21 00:00:00
Tasks: Deep Reinforcement Learning, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep learning techniques have been widely applied, achieving state-of-the-art
results in various fields of study. This survey focuses on deep learning
solutions that target learning control policies for robotics applications. We
carry out our discussions on the two main paradigms for learning control with
deep networks: deep reinforcement learning and imitation learning. For deep
reinforcement learning (DRL), we begin from traditional reinforcement learning
algorithms, showing how they are extended to the deep context and effective
mechanisms that could be added on top of the DRL algorithms. We then introduce
representative works that utilize DRL to solve navigation and manipulation
tasks in robotics. We continue our discussion on methods addressing the
challenge of the reality gap for transferring DRL policies trained in
simulation to real-world scenarios, and summarize robotics simulation platforms
for conducting DRL research. For imitation leaning, we go through its three
main categories, behavior cloning, inverse reinforcement learning and
generative adversarial imitation learning, by introducing their formulations
and their corresponding robotics applications. Finally, we discuss the open
challenges and research frontiers.
Universal and Succinct Source Coding of Deep Neural Networks
Authors: Sourya Basu, Lav R. Varshney
Date: 2018-04-09 00:00:00

Deep neural networks have shown incredible performance for inference tasks in a variety of domains. Unfortunately, most current deep networks are enormous cloud-based structures that require significant storage space, which limits scaling of deep learning as a service (DLaaS) and use for on-device intelligence. This paper is concerned with finding universal lossless compressed representations of deep feedforward networks with synaptic weights drawn from discrete sets, and directly performing inference without full decompression. The basic insight that allows less rate than naive approaches is recognizing that the bipartite graph layers of feedforward networks have a kind of permutation invariance to the labeling of nodes, in terms of inferential operation. We provide efficient algorithms to dissipate this irrelevant uncertainty and then use arithmetic coding to nearly achieve the entropy bound in a universal manner. We also provide experimental results of our approach on several standard datasets.
Deep Learning of the Nonlinear Schrödinger Equation in Fiber-Optic Communications
Authors: Christian Häger, Henry D. Pfister
Date: 2018-04-09 00:00:00

An important problem in fiber-optic communications is to invert the nonlinear
Schr\"odinger equation in real time to reverse the deterministic effects of the
channel. Interestingly, the popular split-step Fourier method (SSFM) leads to a
computation graph that is reminiscent of a deep neural network. This
observation allows one to leverage tools from machine learning to reduce
complexity. In particular, the main disadvantage of the SSFM is that its
complexity using M steps is at least M times larger than a linear equalizer.
This is because the linear SSFM operator is a dense matrix. In previous work,
truncation methods such as frequency sampling, wavelets, or least-squares have
been used to obtain "cheaper" operators that can be implemented using filters.
However, a large number of filter taps are typically required to limit
truncation errors. For example, Ip and Kahn showed that for a 10 Gbaud signal
and 2000 km optical link, a truncated SSFM with 25 steps would require 70-tap
filters in each step and 100 times more operations than linear equalization. We
find that, by jointly optimizing all filters with deep learning, the complexity
can be reduced significantly for similar accuracy. Using optimized 5-tap and
3-tap filters in an alternating fashion, one requires only around 2-6 times the
complexity of linear equalization, depending on the implementation.
Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments
Authors: Rohollah Moghadam, Hamidreza Modares
Date: 2017-08-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

An autonomous and resilient controller is proposed for leader-follower
multi-agent systems under uncertainties and cyber-physical attacks. The leader
is assumed non-autonomous with a nonzero control input, which allows changing
the team behavior or mission in response to environmental changes. A resilient
learning-based control protocol is presented to find optimal solutions to the
synchronization problem in the presence of attacks and system dynamic
uncertainties. An observer-based distributed H_infinity controller is first
designed to prevent propagating the effects of attacks on sensors and actuators
throughout the network, as well as to attenuate the effect of these attacks on
the compromised agent itself. Non-homogeneous game algebraic Riccati equations
are derived to solve the H_infinity optimal synchronization problem and
off-policy reinforcement learning is utilized to learn their solution without
requiring any knowledge of the agent's dynamics. A trust-confidence based
distributed control protocol is then proposed to mitigate attacks that hijack
the entire node and attacks on communication links. A confidence value is
defined for each agent based solely on its local evidence. The proposed
resilient reinforcement learning algorithm employs the confidence value of each
agent to indicate the trustworthiness of its own information and broadcast it
to its neighbors to put weights on the data they receive from it during and
after learning. If the confidence value of an agent is low, it employs a trust
mechanism to identify compromised agents and remove the data it receives from
them from the learning process. Simulation results are provided to show the
effectiveness of the proposed approach.
Active Mini-Batch Sampling using Repulsive Point Processes
Authors: Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi
Date: 2018-04-08 00:00:00
Tasks: Point Processes

The convergence speed of stochastic gradient descent (SGD) can be improved by
actively selecting mini-batches. We explore sampling schemes where similar data
points are less likely to be selected in the same mini-batch. In particular, we
prove that such repulsive sampling schemes lowers the variance of the gradient
estimator. This generalizes recent work on using Determinantal Point Processes
(DPPs) for mini-batch diversification (Zhang et al., 2017) to the broader class
of repulsive point processes. We first show that the phenomenon of variance
reduction by diversified sampling generalizes in particular to non-stationary
point processes. We then show that other point processes may be computationally
much more efficient than DPPs. In particular, we propose and investigate
Poisson Disk sampling---frequently encountered in the computer graphics
community---for this task. We show empirically that our approach improves over
standard SGD both in terms of convergence speed as well as final model
performance.
Estimating Depth from RGB and Sparse Sensing
Authors: Zhao Chen, Vijay Badrinarayanan, Gilad Drozdov, Andrew Rabinovich
Date: 2018-04-08 00:00:00
Proceeding: ECCV 2018 9
Tasks: Depth Estimation, Monocular Depth Estimation

We present a deep model that can accurately produce dense depth maps given an
RGB image with known depth at a very sparse set of pixels. The model works
simultaneously for both indoor/outdoor scenes and produces state-of-the-art
dense depth maps at nearly real-time speeds on both the NYUv2 and KITTI
datasets. We surpass the state-of-the-art for monocular depth estimation even
with depth values for only 1 out of every ~10000 image pixels, and we
outperform other sparse-to-dense depth methods at all sparsity levels. With
depth values for 1/256 of the image pixels, we achieve a mean absolute error of
less than 1% of actual depth on indoor scenes, comparable to the performance of
consumer-grade depth sensor hardware. Our experiments demonstrate that it would
indeed be possible to efficiently transform sparse depth measurements obtained
using e.g. lower-power depth sensors or SLAM systems into high-quality dense
depth maps.
YOLOv3: An Incremental Improvement
Authors: Joseph Redmon, Ali Farhadi
Date: 2018-04-08 00:00:00
Tasks: Classification, Object Detection, One-stage Anchor-free Oriented Object Detection, Pedestrian Detection, Real-Time Object Detection, Robust Object Detection

We present some updates to YOLO! We made a bunch of little design changes to
make it better. We also trained this new network that's pretty swell. It's a
little bigger than last time but more accurate. It's still fast though, don't
worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but
three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3
is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5
mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always,
all the code is online at https://pjreddie.com/yolo/
Best Arm Identification for Contaminated Bandits
Authors: Jason Altschuler, Victor-Emmanuel Brunel, Alan Malek
Date: 2018-02-26 00:00:00
Tasks: Active Learning

This paper studies active learning in the context of robust statistics. Specifically, we propose a variant of the Best Arm Identification problem for \emph{contaminated bandits}, where each arm pull has probability $\varepsilon$ of generating a sample from an arbitrary contamination distribution instead of the true underlying distribution. The goal is to identify the best (or approximately best) true distribution with high probability, with a secondary goal of providing guarantees on the quality of this distribution. The primary challenge of the contaminated bandit setting is that the true distributions are only partially identifiable, even with infinite samples. To address this, we develop tight, non-asymptotic sample complexity bounds for high-probability estimation of the first two robust moments (median and median absolute deviation) from contaminated samples. These concentration inequalities are the main technical contributions of the paper and may be of independent interest. Using these results, we adapt several classical Best Arm Identification algorithms to the contaminated bandit setting and derive sample complexity upper bounds for our problem. Finally, we provide matching information-theoretic lower bounds on the sample complexity (up to a small logarithmic factor).
Comparison of non-linear activation functions for deep neural networks on MNIST classification task
Authors: Dabal Pedamonti
Date: 2018-04-08 00:00:00
Tasks: General Classification

Activation functions play a key role in neural networks so it becomes
fundamental to understand their advantages and disadvantages in order to
achieve better performances. This paper will first introduce common types of
non linear activation functions that are alternative to the well known sigmoid
function and then evaluate their characteristics. Moreover deeper neural
networks will be analysed because they positively influence the final
performances compared to shallower networks. They also strictly depend on the
weight initialisation hence the effect of drawing weights from Gaussian and
uniform distribution will be analysed making particular attention on how the
number of incoming and outgoing connection to a node influence the whole
network.