pwc-archive/papers-with-abstracts huggingface dataset:
Does My Rebuttal Matter? Insights from a Major NLP Conference
Authors: Yang Gao, Steffen Eger, Ilia Kuznetsov, Iryna Gurevych, Yusuke Miyao
Date: 2019-03-27 00:00:00
Proceeding: NAACL 2019 6
Tasks: 4k

Peer review is a core element of the scientific process, particularly in
conference-centered fields such as ML and NLP. However, only few studies have
evaluated its properties empirically. Aiming to fill this gap, we present a
corpus that contains over 4k reviews and 1.2k author responses from ACL-2018.
We quantitatively and qualitatively assess the corpus. This includes a pilot
study on paper weaknesses given by reviewers and on quality of author
responses. We then focus on the role of the rebuttal phase, and propose a novel
task to predict after-rebuttal (i.e., final) scores from initial reviews and
author responses. Although author responses do have a marginal (and
statistically significant) influence on the final scores, especially for
borderline papers, our results suggest that a reviewer's final score is largely
determined by her initial score and the distance to the other reviewers'
initial scores. In this context, we discuss the conformity bias inherent to
peer reviewing, a bias that has largely been overlooked in previous research.
We hope our analyses will help better assess the usefulness of the rebuttal
phase in NLP conferences.
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks
Authors: Francesco Croce, Jonas Rauber, Matthias Hein
Date: 2019-03-27 00:00:00
Tasks: Adversarial Attack

Modern neural networks are highly non-robust against adversarial manipulation. A significant amount of work has been invested in techniques to compute lower bounds on robustness through formal guarantees and to build provably robust models. However, it is still difficult to get guarantees for larger networks or robustness against larger perturbations. Thus attack strategies are needed to provide tight upper bounds on the actual robustness. We significantly improve the randomized gradient-free attack for ReLU networks [9], in particular by scaling it up to large networks. We show that our attack achieves similar or significantly smaller robust accuracy than state-of-the-art attacks like PGD or the one of Carlini and Wagner, thus revealing an overestimation of the robustness by these state-of-the-art methods. Our attack is not based on a gradient descent scheme and in this sense gradient-free, which makes it less sensitive to the choice of hyperparameters as no careful selection of the stepsize is required.
Diversity with Cooperation: Ensemble Methods for Few-Shot Classification
Authors: Nikita Dvornik, Cordelia Schmid, Julien Mairal
Date: 2019-03-27 00:00:00
Proceeding: ICCV 2019 10
Tasks: Classification, Diversity, Few-Shot Image Classification, Few-Shot Learning, General Classification, Meta-Learning

Few-shot classification consists of learning a predictive model that is able to effectively adapt to a new class, given only a few annotated samples. To solve this challenging problem, meta-learning has become a popular paradigm that advocates the ability to "learn to adapt". Recent works have shown, however, that simple learning strategies without meta-learning could be competitive. In this paper, we go a step further and show that by addressing the fundamental high-variance issue of few-shot learning classifiers, it is possible to significantly outperform current meta-learning techniques. Our approach consists of designing an ensemble of deep networks to leverage the variance of the classifiers, and introducing new strategies to encourage the networks to cooperate, while encouraging prediction diversity. Evaluation is conducted on the mini-ImageNet and CUB datasets, where we show that even a single network obtained by distillation yields state-of-the-art results.
Multilevel Text Normalization with Sequence-to-Sequence Networks and Multisource Learning
Authors: Tatyana Ruzsics, Tanja Samardžić
Date: 2019-03-27 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Lemmatization, Text Normalization

We define multilevel text normalization as sequence-to-sequence processing
that transforms naturally noisy text into a sequence of normalized units of
meaning (morphemes) in three steps: 1) writing normalization, 2) lemmatization,
3) canonical segmentation. These steps are traditionally considered separate
NLP tasks, with diverse solutions, evaluation schemes and data sources. We
exploit the fact that all these tasks involve sub-word sequence-to-sequence
transformation to propose a systematic solution for all of them using neural
encoder-decoder technology. The specific challenge that we tackle in this paper
is integrating the traditional know-how on separate tasks into the neural
sequence-to-sequence framework to improve the state of the art. We address this
challenge by enriching the general framework with mechanisms that allow
processing the information on multiple levels of text organization (characters,
morphemes, words, sentences) in combination with structural information
(multilevel language model, part-of-speech) and heterogeneous sources (text,
dictionaries). We show that our solution consistently improves on the current
methods in all three steps. In addition, we analyze the performance of our
system to show the specific contribution of the integrating components to the
overall improvement.
Hierarchical Attention Generative Adversarial Networks for Cross-domain Sentiment Classification
Authors: Yuebing Zhang, Duoqian Miao, Jiaqi Wang
Date: 2019-03-27 00:00:00
Tasks: Classification, Domain Adaptation, General Classification, Sentence, Sentiment Analysis, Sentiment Classification

Cross-domain sentiment classification (CDSC) is an importance task in domain
adaptation and sentiment classification. Due to the domain discrepancy, a
sentiment classifier trained on source domain data may not works well on target
domain data. In recent years, many researchers have used deep neural network
models for cross-domain sentiment classification task, many of which use
Gradient Reversal Layer (GRL) to design an adversarial network structure to
train a domain-shared sentiment classifier. Different from those methods, we
proposed Hierarchical Attention Generative Adversarial Networks (HAGAN) which
alternately trains a generator and a discriminator in order to produce a
document representation which is sentiment-distinguishable but
domain-indistinguishable. Besides, the HAGAN model applies Bidirectional Gated
Recurrent Unit (Bi-GRU) to encode the contextual information of a word and a
sentence into the document representation. In addition, the HAGAN model use
hierarchical attention mechanism to optimize the document representation and
automatically capture the pivots and non-pivots. The experiments on Amazon
review dataset show the effectiveness of HAGAN.
Speed Invariant Time Surface for Learning to Detect Corner Points with Event-Based Cameras
Authors: Jacques Manderscheid, Amos Sironi, Nicolas Bourdis, Davide Migliore, Vincent Lepetit
Date: 2019-03-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: CPU

We propose a learning approach to corner detection for event-based cameras
that is stable even under fast and abrupt motions. Event-based cameras offer
high temporal resolution, power efficiency, and high dynamic range. However,
the properties of event-based data are very different compared to standard
intensity images, and simple extensions of corner detection methods designed
for these images do not perform well on event-based data. We first introduce an
efficient way to compute a time surface that is invariant to the speed of the
objects. We then show that we can train a Random Forest to recognize events
generated by a moving corner from our time surface. Random Forests are also
extremely efficient, and therefore a good choice to deal with the high capture
frequency of event-based cameras ---our implementation processes up to 1.6Mev/s
on a single CPU. Thanks to our time surface formulation and this learning
approach, our method is significantly more robust to abrupt changes of
direction of the corners compared to previous ones. Our method also naturally
assigns a confidence score for the corners, which can be useful for
postprocessing. Moreover, we introduce a high-resolution dataset suitable for
quantitative evaluation and comparison of corner detection methods for
event-based cameras. We call our approach SILC, for Speed Invariant Learned
Corners, and compare it to the state-of-the-art with extensive experiments,
showing better performance.
Active Multi-Information Source Bayesian Quadrature
Authors: Alexandra Gessner, Javier Gonzalez, Maren Mahsereci
Date: 2019-03-27 00:00:00
Tasks: Active Learning

Bayesian quadrature (BQ) is a sample-efficient probabilistic numerical method to solve integrals of expensive-to-evaluate black-box functions, yet so far,active BQ learning schemes focus merely on the integrand itself as information source, and do not allow for information transfer from cheaper, related functions. Here, we set the scene for active learning in BQ when multiple related information sources of variable cost (in input and source) are accessible. This setting arises for example when evaluating the integrand requires a complex simulation to be run that can be approximated by simulating at lower levels of sophistication and at lesser expense. We construct meaningful cost-sensitive multi-source acquisition rates as an extension to common utility functions from vanilla BQ (VBQ),and discuss pitfalls that arise from blindly generalizing. Furthermore, we show that the VBQ acquisition policy is a corner-case of all considered cost-sensitive acquisition schemes, which collapse onto one single de-generate policy in the case of one source and constant cost. In proof-of-concept experiments we scrutinize the behavior of our generalized acquisition functions. On an epidemiological model, we demonstrate that active multi-source BQ (AMS-BQ) allocates budget more efficiently than VBQ for learning the integral to a good accuracy.
Generalized Off-Policy Actor-Critic
Authors: Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson
Date: 2019-03-27 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: counterfactual, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a new objective, the counterfactual objective, unifying existing objectives for off-policy policy gradient algorithms in the continuing reinforcement learning (RL) setting. Compared to the commonly used excursion objective, which can be misleading about the performance of the target policy when deployed, our new objective better predicts such performance. We prove the Generalized Off-Policy Policy Gradient Theorem to compute the policy gradient of the counterfactual objective and use an emphatic approach to get an unbiased sample from this policy gradient, yielding the Generalized Off-Policy Actor-Critic (Geoff-PAC) algorithm. We demonstrate the merits of Geoff-PAC over existing algorithms in Mujoco robot simulation tasks, the first empirical success of emphatic algorithms in prevailing deep RL benchmarks.
Rethinking the Evaluation of Video Summaries
Authors: Mayu Otani, Yuta Nakashima, Esa Rahtu, Janne Heikkilä
Date: 2019-03-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Video Segmentation, Video Semantic Segmentation, Video Summarization

Video summarization is a technique to create a short skim of the original
video while preserving the main stories/content. There exists a substantial
interest in automatizing this process due to the rapid growth of the available
material. The recent progress has been facilitated by public benchmark
datasets, which enable easy and fair comparison of methods. Currently the
established evaluation protocol is to compare the generated summary with
respect to a set of reference summaries provided by the dataset. In this paper,
we will provide in-depth assessment of this pipeline using two popular
benchmark datasets. Surprisingly, we observe that randomly generated summaries
achieve comparable or better performance to the state-of-the-art. In some
cases, the random summaries outperform even the human generated summaries in
leave-one-out experiments. Moreover, it turns out that the video segmentation,
which is often considered as a fixed pre-processing method, has the most
significant impact on the performance measure. Based on our observations, we
propose alternative approaches for assessing the importance scores as well as
an intuitive visualization of correlation between the estimated scoring and
human annotations.
Dense Intrinsic Appearance Flow for Human Pose Transfer
Authors: Yining Li, Chen Huang, Chen Change Loy
Date: 2019-03-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Pose Transfer

We present a novel approach for the task of human pose transfer, which aims
at synthesizing a new image of a person from an input image of that person and
a target pose. We address the issues of limited correspondences identified
between keypoints only and invisible pixels due to self-occlusion. Unlike
existing methods, we propose to estimate dense and intrinsic 3D appearance flow
to better guide the transfer of pixels between poses. In particular, we wish to
generate the 3D flow from just the reference and target poses. Training a
network for this purpose is non-trivial, especially when the annotations for 3D
appearance flow are scarce by nature. We address this problem through a flow
synthesis stage. This is achieved by fitting a 3D model to the given pose pair
and project them back to the 2D plane to compute the dense appearance flow for
training. The synthesized ground-truths are then used to train a feedforward
network for efficient mapping from the input and target skeleton poses to the
3D appearance flow. With the appearance flow, we perform feature warping on the
input image and generate a photorealistic image of the target pose. Extensive
results on DeepFashion and Market-1501 datasets demonstrate the effectiveness
of our approach over existing methods. Our code is available at
http://mmlab.ie.cuhk.edu.hk/projects/pose-transfer
A novel machine learning based framework for detection of Autism Spectrum Disorder (ASD)
Authors: Hamza Sharif, Rizwan Ahmed Khan
Date: 2019-03-27 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning, Deep Learning, Transfer Learning

Computer vision and machine learning are the linchpin of field of automation. The medicine industry has adopted numerous methods to discover the root causes of many diseases in order to automate detection process. But, the biomarkers of Autism Spectrum Disorder (ASD) are still unknown, let alone automating its detection. Studies from the neuroscience domain highlighted the fact that corpus callosum and intracranial brain volume holds significant information for detection of ASD. Such results and studies are not tested and verified by scientists working in the domain of computer vision / machine learning. Thus, in this study we have proposed a machine learning based framework for automatic detection of ASD using features extracted from corpus callosum and intracranial brain volume from ABIDE dataset. Corpus callosum and intracranial brain volume data is obtained from T1-weighted MRI scans. Our proposed framework first calculates weights of features extracted from Corpus callosum and intracranial brain volume data. This step ensures to utilize discriminative capabilities of only those features that will help in robust recognition of ASD. Then, conventional machine learning algorithm (conventional refers to algorithms other than deep learning) is applied on features that are most significant in terms of discriminative capabilities for recognition of ASD. Finally, for benchmarking and to verify potential of deep learning on analyzing neuroimaging data i.e. T1-weighted MRI scans, we have done experiment with state of the art deep learning architecture i.e. VGG16 . We have used transfer learning approach to use already trained VGG16 model for detection of ASD. This is done to help readers understand benefits and bottlenecks of using deep learning approach for analyzing neuroimaging data which is difficult to record in large enough quantity for deep learning.
Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Tools
Authors: Ruben Mayer, Hans-Arno Jacobsen
Date: 2019-03-27 00:00:00
Tasks: Deep Learning, Management, Scheduling

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-art results in various domains such as image recognition and natural language processing. One of the reasons for this success is the increasing size of DL models and the proliferation of vast amounts of training data being available. To keep on improving the performance of DL, increasing the scalability of DL systems is necessary. In this survey, we perform a broad and thorough investigation on challenges, techniques and tools for scalable DL on distributed infrastructures. This incorporates infrastructures for DL, methods for parallel DL training, multi-tenant resource scheduling and the management of training and model data. Further, we analyze and compare 11 current open-source DL frameworks and tools and investigate which of the techniques are commonly implemented in practice. Finally, we highlight future research trends in DL systems that deserve further research.
Linkage Based Face Clustering via Graph Convolution Network
Authors: Zhongdao Wang, Liang Zheng, Ya-Li Li, Shengjin Wang
Date: 2019-03-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Clustering, Face Clustering, Link Prediction

In this paper, we present an accurate and scalable approach to the face
clustering task. We aim at grouping a set of faces by their potential
identities. We formulate this task as a link prediction problem: a link exists
between two faces if they are of the same identity. The key idea is that we
find the local context in the feature space around an instance (face) contains
rich information about the linkage relationship between this instance and its
neighbors. By constructing sub-graphs around each instance as input data, which
depict the local context, we utilize the graph convolution network (GCN) to
perform reasoning and infer the likelihood of linkage between pairs in the
sub-graphs. Experiments show that our method is more robust to the complex
distribution of faces than conventional methods, yielding favorably comparable
results to state-of-the-art methods on standard face clustering benchmarks, and
is scalable to large datasets. Furthermore, we show that the proposed method
does not need the number of clusters as prior, is aware of noises and outliers,
and can be extended to a multi-view version for more accurate clustering
accuracy.
3D Face Mask Presentation Attack Detection Based on Intrinsic Image Analysis
Authors: Lei Li, Zhaoqiang Xia, Xiaoyue Jiang, Yupeng Ma, Fabio Roli, Xiaoyi Feng
Date: 2019-03-27 00:00:00
Tasks: Face Recognition, Intrinsic Image Decomposition

Face presentation attacks have become a major threat to face recognition
systems and many countermeasures have been proposed in the past decade.
However, most of them are devoted to 2D face presentation attacks, rather than
3D face masks. Unlike the real face, the 3D face mask is usually made of resin
materials and has a smooth surface, resulting in reflectance differences. So,
we propose a novel detection method for 3D face mask presentation attack by
modeling reflectance differences based on intrinsic image analysis. In the
proposed method, the face image is first processed with intrinsic image
decomposition to compute its reflectance image. Then, the intensity
distribution histograms are extracted from three orthogonal planes to represent
the intensity differences of reflectance images between the real face and 3D
face mask. After that, the 1D convolutional network is further used to capture
the information for describing different materials or surfaces react
differently to changes in illumination. Extensive experiments on the 3DMAD
database demonstrate the effectiveness of our proposed method in distinguishing
a face mask from the real one and show that the detection performance
outperforms other state-of-the-art methods.
Image search using multilingual texts: a cross-modal learning approach between image and text
Authors: Maxime Portaz, Hicham Randrianarivo, Adrien Nivaggioli, Estelle Maudet, Christophe Servan, Sylvain Peyronnet
Date: 2019-03-27 00:00:00
Tasks: Image Retrieval

Multilingual (or cross-lingual) embeddings represent several languages in a unique vector space. Using a common embedding space enables for a shared semantic between words from different languages. In this paper, we propose to embed images and texts into a unique distributional vector space, enabling to search images by using text queries expressing information needs related to the (visual) content of images, as well as using image similarity. Our framework forces the representation of an image to be similar to the representation of the text that describes it. Moreover, by using multilingual embeddings we ensure that words from two different languages have close descriptors and thus are attached to similar images. We provide experimental evidence of the efficiency of our approach by experimenting it on two datasets: Common Objects in COntext (COCO) [19] and Multi30K [7].
Deformable kernel networks for guided depth map upsampling
Authors: Beomjun Kim, Jean Ponce, Bumsub Ham
Date: 2019-03-27 00:00:00

We address the problem of upsampling a low-resolution (LR) depth map using a
registered high-resolution (HR) color image of the same scene. Previous methods
based on convolutional neural networks (CNNs) combine nonlinear activations of
spatially-invariant kernels to estimate structural details from LR depth and HR
color images, and regress upsampling results directly from the networks. In
this paper, we revisit the weighted averaging process that has been widely used
to transfer structural details from hand-crafted visual features to LR depth
maps. We instead learn explicitly sparse and spatially-variant kernels for this
task. To this end, we propose a CNN architecture and its efficient
implementation, called the deformable kernel network (DKN), that outputs sparse
sets of neighbors and the corresponding weights adaptively for each pixel. We
also propose a fast version of DKN (FDKN) that runs about 17 times faster (0.01
seconds for a HR image of size 640 x 480). Experimental results on standard
benchmarks demonstrate the effectiveness of our approach. In particular, we
show that the weighted averaging process with 3 x 3 kernels (i.e., aggregating
9 samples sparsely chosen) outperforms the state of the art by a significant
margin.
Grammatical Error Correction and Style Transfer via Zero-shot Monolingual Translation
Authors: Elizaveta Korotkova, Agnes Luhtaru, Maksym Del, Krista Liin, Daiga Deksne, Mark Fishel
Date: 2019-03-27 00:00:00
Tasks: Grammatical Error Correction, Style Transfer, Text Style Transfer, Translation

Both grammatical error correction and text style transfer can be viewed as monolingual sequence-to-sequence transformation tasks, but the scarcity of directly annotated data for either task makes them unfeasible for most languages. We present an approach that does both tasks within the same trained model, and only uses regular language parallel data, without requiring error-corrected or style-adapted texts. We apply our model to three languages and present a thorough evaluation on both tasks, showing that the model is reliable for a number of error types and style transfer aspects.