pwc-archive/papers-with-abstracts huggingface dataset:
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
Authors: Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, Yoshua Bengio
Date: 2016-11-28 00:00:00
Tasks: image-classification, Image Segmentation, Segmentation, Semantic Segmentation

State-of-the-art approaches for semantic image segmentation are built on
Convolutional Neural Networks (CNNs). The typical segmentation architecture is
composed of (a) a downsampling path responsible for extracting coarse semantic
features, followed by (b) an upsampling path trained to recover the input image
resolution at the output of the model and, optionally, (c) a post-processing
module (e.g. Conditional Random Fields) to refine the model predictions.
  Recently, a new CNN architecture, Densely Connected Convolutional Networks
(DenseNets), has shown excellent results on image classification tasks. The
idea of DenseNets is based on the observation that if each layer is directly
connected to every other layer in a feed-forward fashion then the network will
be more accurate and easier to train.
  In this paper, we extend DenseNets to deal with the problem of semantic
segmentation. We achieve state-of-the-art results on urban scene benchmark
datasets such as CamVid and Gatech, without any further post-processing module
nor pretraining. Moreover, due to smart construction of the model, our approach
has much less parameters than currently published best entries for these
datasets.
  Code to reproduce the experiments is available here :
https://github.com/SimJeg/FC-DenseNet/blob/master/train.py
Accelerate RNN-based Training with Importance Sampling
Authors: Fei Wang, Xiaofeng Gao, Guihai Chen, Jun Ye
Date: 2017-10-31 00:00:00
Tasks: Stochastic Optimization

Importance sampling (IS) as an elegant and efficient variance reduction (VR)
technique for the acceleration of stochastic optimization problems has
attracted many researches recently. Unlike commonly adopted stochastic uniform
sampling in stochastic optimizations, IS-integrated algorithms sample training
data at each iteration with respect to a weighted sampling probability
distribution $P$, which is constructed according to the precomputed importance
factors. Previous experimental results show that IS has achieved remarkable
progresses in the acceleration of training convergence. Unfortunately, the
calculation of the sampling probability distribution $P$ causes a major
limitation of IS: it requires the input data to be well-structured, i.e., the
feature vector is properly defined. Consequently, recurrent neural networks
(RNN) as a popular learning algorithm is not able to enjoy the benefits of IS
due to the fact that its raw input data, i.e., the training sequences, are
often unstructured which makes calculation of $P$ impossible. In considering of
the the popularity of RNN-based learning applications and their relative long
training time, we are interested in accelerating them through IS. This paper
propose a novel Fast-Importance-Mining algorithm to calculate the importance
factor for unstructured data which makes the application of IS in RNN-based
applications possible. Our experimental evaluation on popular open-source
RNN-based learning applications validate the effectiveness of IS in improving
the convergence rate of RNNs.
Deep Hashing with Triplet Quantization Loss
Authors: Yuefu Zhou, Shanshan Huang, Ya zhang, Yan-Feng Wang
Date: 2017-10-31 00:00:00
Tasks: Deep Hashing, Image Retrieval, Quantization, Retrieval, Triplet

With the explosive growth of image databases, deep hashing, which learns
compact binary descriptors for images, has become critical for fast image
retrieval. Many existing deep hashing methods leverage quantization loss,
defined as distance between the features before and after quantization, to
reduce the error from binarizing features. While minimizing the quantization
loss guarantees that quantization has minimal effect on retrieval accuracy, it
unfortunately significantly reduces the expressiveness of features even before
the quantization. In this paper, we show that the above definition of
quantization loss is too restricted and in fact not necessary for maintaining
high retrieval accuracy. We therefore propose a new form of quantization loss
measured in triplets. The core idea of the triplet quantization loss is to
learn discriminative real-valued descriptors which lead to minimal loss on
retrieval accuracy after quantization. Extensive experiments on two widely used
benchmark data sets of different scales, CIFAR-10 and In-shop, demonstrate that
the proposed method outperforms the state-of-the-art deep hashing methods.
Moreover, we show that the compact binary descriptors obtained with triplet
quantization loss lead to very small performance drop after quantization.
Regret Minimization for Partially Observable Deep Reinforcement Learning
Authors: Peter Jin, Kurt Keutzer, Sergey Levine
Date: 2017-10-31 00:00:00
Proceeding: ICML 2018 7
Tasks: counterfactual, Deep Reinforcement Learning, Minecraft, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning algorithms that estimate state and state-action
value functions have been shown to be effective in a variety of challenging
domains, including learning control strategies from raw image pixels. However,
algorithms that estimate state and state-action value functions typically
assume a fully observed state and must compensate for partial observations by
using finite length observation histories or recurrent networks. In this work,
we propose a new deep reinforcement learning algorithm based on counterfactual
regret minimization that iteratively updates an approximation to an
advantage-like function and is robust to partially observed state. We
demonstrate that this new algorithm can substantially outperform strong
baseline methods on several partially observed reinforcement learning tasks:
learning first-person 3D navigation in Doom and Minecraft, and acting in the
presence of partially observed objects in Doom and Pong.
The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings
Authors: Tomer Galanti, Lior Wolf, Sagie Benaim
Date: 2017-08-31 00:00:00
Proceeding: ICLR 2018 1

We discuss the feasibility of the following learning problem: given unmatched samples from two domains and nothing else, learn a mapping between the two, which preserves semantics. Due to the lack of paired samples and without any definition of the semantic information, the problem might seem ill-posed. Specifically, in typical cases, it seems possible to build infinitely many alternative mappings from every target mapping. This apparent ambiguity stands in sharp contrast to the recent empirical success in solving this problem. We identify the abstract notion of aligning two domains in a semantic way with concrete terms of minimal relative complexity. A theoretical framework for measuring the complexity of compositions of functions is developed in order to show that it is reasonable to expect the minimal complexity mapping to be unique. The measured complexity used is directly related to the depth of the neural networks being learned and a semantically aligned mapping could then be captured simply by learning using architectures that are not much bigger than the minimal architecture. Various predictions are made based on the hypothesis that semantic alignment can be captured by the minimal mapping. These are verified extensively. In addition, a new mapping algorithm is proposed and shown to lead to better mapping results.
Learned Features are better for Ethnicity Classification
Authors: Inzamam Anwar, Naeem Ul Islam
Date: 2017-09-21 00:00:00
Tasks: Attribute, Classification, General Classification

Ethnicity is a key demographic attribute of human beings and it plays a vital
role in automatic facial recognition and have extensive real world applications
such as Human Computer Interaction (HCI); demographic based classification;
biometric based recognition; security and defense to name a few. In this paper
we present a novel approach for extracting ethnicity from the facial images.
The proposed method makes use of a pre trained Convolutional Neural Network
(CNN) to extract the features and then Support Vector Machine (SVM) with linear
kernel is used as a classifier. This technique uses translational invariant
hierarchical features learned by the network, in contrast to previous works,
which use hand crafted features such as Local Binary Pattern (LBP); Gabor etc.
Thorough experiments are presented on ten different facial databases which
strongly suggest that our approach is robust to different expressions and
illuminations conditions. Here we consider ethnicity classification as a three
class problem including Asian, African-American and Caucasian. Average
classification accuracy over all databases is 98.28%, 99.66% and 99.05% for
Asian, African-American and Caucasian respectively.
Discovering Causal Signals in Images
Authors: David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou
Date: 2016-05-26 00:00:00
Proceeding: CVPR 2017 7
Tasks: Causal Discovery

This paper establishes the existence of observable footprints that reveal the
"causal dispositions" of the object categories appearing in collections of
images. We achieve this goal in two steps. First, we take a learning approach
to observational causal discovery, and build a classifier that achieves
state-of-the-art performance on finding the causal direction between pairs of
random variables, given samples from their joint distribution. Second, we use
our causal direction classifier to effectively distinguish between features of
objects and features of their contexts in collections of static images. Our
experiments demonstrate the existence of a relation between the direction of
causality and the difference between objects and their contexts, and by the
same token, the existence of observable signals that reveal the causal
dispositions of objects.
Updating the VESICLE-CNN Synapse Detector
Authors: Andrew Warrington, Frank Wood
Date: 2017-10-31 00:00:00

We present an updated version of the VESICLE-CNN algorithm presented by
Roncal et al. (2014). The original implementation makes use of a patch-based
approach. This methodology is known to be slow due to repeated computations. We
update this implementation to be fully convolutional through the use of dilated
convolutions, recovering the expanded field of view achieved through the use of
strided maxpools, but without a degradation of spatial resolution. This updated
implementation performs as well as the original implementation, but with a
$600\times$ speedup at test time. We release source code and data into the
public domain.
A Foundry of Human Activities and Infrastructures
Authors: Robert B. Allen, Eunsang Yang, Tatsawan Timakum
Date: 2017-10-31 00:00:00

Direct representation knowledgebases can enhance and even provide an
alternative to document-centered digital libraries. Here we consider realist
semantic modeling of everyday activities and infrastructures in such
knowledgebases. Because we want to integrate a wide variety of topics, a
collection of ontologies (a foundry) and a range of other knowledge resources
are needed. We first consider modeling the routine procedures that support
human activities and technologies. Next, we examine the interactions of
technologies with aspects of social organization. Then, we consider approaches
and issues for developing and validating explanations of the relationships
among various entities.
Vehicle classification based on convolutional networks applied to FM-CW radar signals
Authors: Samuele Capobianco, Luca Facheris, Fabrizio Cuccoli, Simone Marinai
Date: 2017-10-09 00:00:00
Tasks: General Classification

This paper investigates the processing of Frequency Modulated-Continuos Wave
(FM-CW) radar signals for vehicle classification. In the last years deep
learning has gained interest in several scientific fields and signal processing
is not one exception. In this work we address the recognition of the vehicle
category using a Convolutional Neural Network (CNN) applied to range Doppler
signature. The developed system first transforms the 1-dimensional signal into
a 3-dimensional signal that is subsequently used as input to the CNN. When
using the trained model to predict the vehicle category we obtain good
performance.
Post Training in Deep Learning with Last Kernel
Authors: Thomas Moreau, Julien Audiffren
Date: 2016-11-14 00:00:00
Tasks: Deep Learning, Unsupervised Pre-training

One of the main challenges of deep learning methods is the choice of an
appropriate training strategy. In particular, additional steps, such as
unsupervised pre-training, have been shown to greatly improve the performances
of deep structures. In this article, we propose an extra training step, called
post-training, which only optimizes the last layer of the network. We show that
this procedure can be analyzed in the context of kernel theory, with the first
layers computing an embedding of the data and the last layer a statistical
model to solve the task based on this embedding. This step makes sure that the
embedding, or representation, of the data is used in the best possible way for
the considered task. This idea is then tested on multiple architectures with
various data sets, showing that it consistently provides a boost in
performance.
Parametrizing filters of a CNN with a GAN
Authors: Yannic Kilcher, Gary Becigneul, Thomas Hofmann
Date: 2017-10-31 00:00:00
Proceeding: ICLR 2018 1
Tasks: Generative Adversarial Network

It is commonly agreed that the use of relevant invariances as a good
statistical bias is important in machine-learning. However, most approaches
that explicitly incorporate invariances into a model architecture only make use
of very simple transformations, such as translations and rotations. Hence,
there is a need for methods to model and extract richer transformations that
capture much higher-level invariances. To that end, we introduce a tool
allowing to parametrize the set of filters of a trained convolutional neural
network with the latent space of a generative adversarial network. We then show
that the method can capture highly non-linear invariances of the data by
visualizing their effect in the data space.
Scavenger 0.1: A Theorem Prover Based on Conflict Resolution
Authors: Daniyar Itegulov, John Slaney, Bruno Woltzenlogel Paleo
Date: 2017-04-11 00:00:00

This paper introduces Scavenger, the first theorem prover for pure
first-order logic without equality based on the new conflict resolution
calculus. Conflict resolution has a restricted resolution inference rule that
resembles (a first-order generalization of) unit propagation as well as a rule
for assuming decision literals and a rule for deriving new clauses by (a
first-order generalization of) conflict-driven clause learning.
A Computer Vision System to Localize and Classify Wastes on the Streets
Authors: Mohammad Saeed Rad, Andreas von Kaenel, Andre Droux, Francois Tieche, Nabil Ouerhani, Hazim Kemal Ekenel, Jean-Philippe Thiran
Date: 2017-10-31 00:00:00
Tasks: Benchmarking

Littering quantification is an important step for improving cleanliness of
cities. When human interpretation is too cumbersome or in some cases
impossible, an objective index of cleanliness could reduce the littering by
awareness actions. In this paper, we present a fully automated computer vision
application for littering quantification based on images taken from the streets
and sidewalks. We have employed a deep learning based framework to localize and
classify different types of wastes. Since there was no waste dataset available,
we built our acquisition system mounted on a vehicle. Collected images
containing different types of wastes. These images are then annotated for
training and benchmarking the developed system. Our results on real case
scenarios show accurate detection of littering on variant backgrounds.
Image Patch Matching Using Convolutional Descriptors with Euclidean Distance
Authors: Iaroslav Melekhov, Juho Kannala, Esa Rahtu
Date: 2017-10-31 00:00:00
Tasks: object-detection, Object Detection, Patch Matching

In this work we propose a neural network based image descriptor suitable for
image patch matching, which is an important task in many computer vision
applications. Our approach is influenced by recent success of deep
convolutional neural networks (CNNs) in object detection and classification
tasks. We develop a model which maps the raw input patch to a low dimensional
feature vector so that the distance between representations is small for
similar patches and large otherwise. As a distance metric we utilize L2 norm,
i.e. Euclidean distance, which is fast to evaluate and used in most popular
hand-crafted descriptors, such as SIFT. According to the results, our approach
outperforms state-of-the-art L2-based descriptors and can be considered as a
direct replacement of SIFT. In addition, we conducted experiments with batch
normalization and histogram equalization as a preprocessing method of the input
data. The results confirm that these techniques further improve the performance
of the proposed descriptor. Finally, we show promising preliminary results by
appending our CNNs with recently proposed spatial transformer networks and
provide a visualisation and interpretation of their impact.
Common Representation Learning Using Step-based Correlation Multi-Modal CNN
Authors: Gaurav Bhatt, Piyush Jha, Balasubramanian Raman
Date: 2017-10-31 00:00:00
Tasks: Representation Learning, Transfer Learning

Deep learning techniques have been successfully used in learning a common
representation for multi-view data, wherein the different modalities are
projected onto a common subspace. In a broader perspective, the techniques used
to investigate common representation learning falls under the categories of
canonical correlation-based approaches and autoencoder based approaches. In
this paper, we investigate the performance of deep autoencoder based methods on
multi-view data. We propose a novel step-based correlation multi-modal CNN
(CorrMCNN) which reconstructs one view of the data given the other while
increasing the interaction between the representations at each hidden layer or
every intermediate step. Finally, we evaluate the performance of the proposed
model on two benchmark datasets - MNIST and XRMB. Through extensive
experiments, we find that the proposed model achieves better performance than
the current state-of-the-art techniques on joint common representation learning
and transfer learning tasks.
Spatio-temporal interaction model for crowd video analysis
Authors: Neha Bhargava, Subhasis Chaudhuri
Date: 2017-10-31 00:00:00
Tasks: model

We present an unsupervised approach to analyze crowd at various levels of
granularity $-$ individual, group and collective. We also propose a motion
model to represent the collective motion of the crowd. The model captures the
spatio-temporal interaction pattern of the crowd from the trajectory data
captured over a time period. Furthermore, we also propose an effective group
detection algorithm that utilizes the eigenvectors of the interaction matrix of
the model. We also show that the eigenvalues of the interaction matrix
characterize various group activities such as being stationary, walking,
splitting and approaching. The algorithm is also extended trivially to
recognize individual activity. Finally, we discover the overall crowd behavior
by classifying a crowd video in one of the eight categories. Since the crowd
behavior is determined by its constituent groups, we demonstrate the usefulness
of group level features during classification. Extensive experimentation on
various datasets demonstrates a superlative performance of our algorithms over
the state-of-the-art methods.
ChainerMN: Scalable Distributed Deep Learning Framework
Authors: Takuya Akiba, Keisuke Fukuda, Shuji Suzuki
Date: 2017-10-31 00:00:00
Tasks: Deep Learning

One of the keys for deep learning to have made a breakthrough in various
fields was to utilize high computing powers centering around GPUs. Enabling the
use of further computing abilities by distributed processing is essential not
only to make the deep learning bigger and faster but also to tackle unsolved
challenges. We present the design, implementation, and evaluation of ChainerMN,
the distributed deep learning framework we have developed. We demonstrate that
ChainerMN can scale the learning process of the ResNet-50 model to the ImageNet
dataset up to 128 GPUs with the parallel efficiency of 90%.
Tensor Regression Meets Gaussian Processes
Authors: Rose Yu, Guangyu Li, Yan Liu
Date: 2017-10-31 00:00:00
Tasks: Bayesian Inference, Gaussian Processes, regression

Low-rank tensor regression, a new model class that learns high-order
correlation from data, has recently received considerable attention. At the
same time, Gaussian processes (GP) are well-studied machine learning models for
structure learning. In this paper, we demonstrate interesting connections
between the two, especially for multi-way data analysis. We show that low-rank
tensor regression is essentially learning a multi-linear kernel in Gaussian
processes, and the low-rank assumption translates to the constrained Bayesian
inference problem. We prove the oracle inequality and derive the average case
learning curve for the equivalent GP model. Our finding implies that low-rank
tensor regression, though empirically successful, is highly dependent on the
eigenvalues of covariance functions as well as variable correlations.
Riemannian approach to batch normalization
Authors: Minhyung Cho, Jaehyung Lee
Date: 2017-09-27 00:00:00
Proceeding: NeurIPS 2017 12

Batch Normalization (BN) has proven to be an effective algorithm for deep
neural network training by normalizing the input to each neuron and reducing
the internal covariate shift. The space of weight vectors in the BN layer can
be naturally interpreted as a Riemannian manifold, which is invariant to linear
scaling of weights. Following the intrinsic geometry of this manifold provides
a new learning rule that is more efficient and easier to analyze. We also
propose intuitive and effective gradient clipping and regularization methods
for the proposed algorithm by utilizing the geometry of the manifold. The
resulting algorithm consistently outperforms the original BN on various types
of network architectures and datasets.