pwc-archive/papers-with-abstracts huggingface dataset:
Noise-Aware Unsupervised Deep Lidar-Stereo Fusion
Authors: Xuelian Cheng, Yiran Zhong, Yuchao Dao, Pan Ji, Hongdong Li
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D geometry, Depth Completion, Stereo Matching, Stereo Matching Hand

In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo
fusion network, which can be trained in an end-to-end manner without the need
of ground truth depth maps. By introducing a novel "Feedback Loop'' to connect
the network input with output, LidarStereoNet could tackle both noisy Lidar
points and misalignment between sensors that have been ignored in existing
Lidar-stereo fusion studies. Besides, we propose to incorporate a piecewise
planar model into network learning to further constrain depths to conform to
the underlying 3D geometry. Extensive quantitative and qualitative evaluations
on both real and synthetic datasets demonstrate the superiority of our method,
which outperforms state-of-the-art stereo matching, depth completion and
Lidar-Stereo fusion approaches significantly.
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability
Authors: Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
Date: 2019-04-08 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning

Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can be applied regardless of model complexity, they can produce misleading and verbose results if the model is too complex, especially w.r.t. feature interactions. To quantify the complexity of arbitrary machine learning models, we propose model-agnostic complexity measures based on functional decomposition: number of features used, interaction strength and main effect complexity. We show that post-hoc interpretation of models that minimize the three measures is more reliable and compact. Furthermore, we demonstrate the application of these measures in a multi-objective optimization approach which simultaneously minimizes loss and complexity.
On the Learnability of Deep Random Networks
Authors: Abhimanyu Das, Sreenivas Gollapudi, Ravi Kumar, Rina Panigrahy
Date: 2019-04-08 00:00:00

In this paper we study the learnability of deep random networks from both
theoretical and practical points of view. On the theoretical front, we show
that the learnability of random deep networks with sign activation drops
exponentially with its depth. On the practical front, we find that the
learnability drops sharply with depth even with the state-of-the-art training
methods, suggesting that our stylized theoretical results are closer to
reality.
The Kikuchi Hierarchy and Tensor PCA
Authors: Alexander S. Wein, Ahmed El Alaoui, Cristopher Moore
Date: 2019-04-08 00:00:00
Tasks: Bayesian Inference

For the tensor PCA (principal component analysis) problem, we propose a new hierarchy of increasingly powerful algorithms with increasing runtime. Our hierarchy is analogous to the sum-of-squares (SOS) hierarchy but is instead inspired by statistical physics and related algorithms such as belief propagation and AMP (approximate message passing). Our level-$\ell$ algorithm can be thought of as a linearized message-passing algorithm that keeps track of $\ell$-wise dependencies among the hidden variables. Specifically, our algorithms are spectral methods based on the Kikuchi Hessian, which generalizes the well-studied Bethe Hessian to the higher-order Kikuchi free energies. It is known that AMP, the flagship algorithm of statistical physics, has substantially worse performance than SOS for tensor PCA. In this work we 'redeem' the statistical physics approach by showing that our hierarchy gives a polynomial-time algorithm matching the performance of SOS. Our hierarchy also yields a continuum of subexponential-time algorithms, and we prove that these achieve the same (conjecturally optimal) tradeoff between runtime and statistical power as SOS. Our proofs are much simpler than prior work, and also apply to the related problem of refuting random $k$-XOR formulas. The results we present here apply to tensor PCA for tensors of all orders, and to $k$-XOR when $k$ is even. Our methods suggest a new avenue for systematically obtaining optimal algorithms for Bayesian inference problems, and our results constitute a step toward unifying the statistical physics and sum-of-squares approaches to algorithm design.
Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization
Authors: Chao Huang, Haojie Liu, Tong Chen, Qiu Shen, Zhan Ma
Date: 2019-04-08 00:00:00
Tasks: Generative Adversarial Network, Image Compression

We propose a MultiScale AutoEncoder(MSAE) based extreme image compression framework to offer visually pleasing reconstruction at a very low bitrate. Our method leverages the "priors" at different resolution scale to improve the compression efficiency, and also employs the generative adversarial network(GAN) with multiscale discriminators to perform the end-to-end trainable rate-distortion optimization. We compare the perceptual quality of our reconstructions with traditional compression algorithms using High-Efficiency Video Coding(HEVC) based Intra Profile and JPEG2000 on the public Cityscapes and ADE20K datasets, demonstrating the significant subjective quality improvement.
Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes
Authors: Yiran Zhong, Pan Ji, Jianyuan Wang, Yuchao Dai, Hongdong Li
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Benchmarking, Deep Learning, Optical Flow Estimation

Unsupervised deep learning for optical flow computation has achieved promising results. Most existing deep-net based methods rely on image brightness consistency and local smoothness constraint to train the networks. Their performance degrades at regions where repetitive textures or occlusions occur. In this paper, we propose Deep Epipolar Flow, an unsupervised optical flow method which incorporates global geometric constraints into network learning. In particular, we investigate multiple ways of enforcing the epipolar constraint in flow estimation. To alleviate a "chicken-and-egg" type of problem encountered in dynamic scenes where multiple motions may be present, we propose a low-rank constraint as well as a union-of-subspaces constraint for training. Experimental results on various benchmarking datasets show that our method achieves competitive performance compared with supervised methods and outperforms state-of-the-art unsupervised deep-learning methods.
Decomposition-Based Transfer Distance Metric Learning for Image Classification
Authors: Yong Luo, Tongliang Liu, DaCheng Tao, Chao Xu
Date: 2019-04-08 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Metric Learning, Transfer Learning

Distance metric learning (DML) is a critical factor for image analysis and
pattern recognition. To learn a robust distance metric for a target task, we
need abundant side information (i.e., the similarity/dissimilarity pairwise
constraints over the labeled data), which is usually unavailable in practice
due to the high labeling cost. This paper considers the transfer learning
setting by exploiting the large quantity of side information from certain
related, but different source tasks to help with target metric learning (with
only a little side information). The state-of-the-art metric learning
algorithms usually fail in this setting because the data distributions of the
source task and target task are often quite different. We address this problem
by assuming that the target distance metric lies in the space spanned by the
eigenvectors of the source metrics (or other randomly generated bases). The
target metric is represented as a combination of the base metrics, which are
computed using the decomposed components of the source metrics (or simply a set
of random bases); we call the proposed method, decomposition-based transfer DML
(DTDML). In particular, DTDML learns a sparse combination of the base metrics
to construct the target metric by forcing the target metric to be close to an
integration of the source metrics. The main advantage of the proposed method
compared with existing transfer metric learning approaches is that we directly
learn the base metric coefficients instead of the target metric. To this end,
far fewer variables need to be learned. We therefore obtain more reliable
solutions given the limited side information and the optimization tends to be
faster. Experiments on the popular handwritten image (digit, letter)
classification and challenge natural image annotation tasks demonstrate the
effectiveness of the proposed method.
Weakly Supervised Person Re-ID: Differentiable Graphical Learning and A New Benchmark
Authors: Guangrun Wang, Guangcong Wang, Xujie Zhang, Jian-Huang Lai, Zhengtao Yu, Liang Lin
Date: 2019-04-08 00:00:00
Tasks: Person Re-Identification, Pseudo Label

Person re-identification (Re-ID) benefits greatly from the accurate annotations of existing datasets (e.g., CUHK03 [1] and Market-1501 [2]), which are quite expensive because each image in these datasets has to be assigned with a proper label. In this work, we ease the annotation of Re-ID by replacing the accurate annotation with inaccurate annotation, i.e., we group the images into bags in terms of time and assign a bag-level label for each bag. This greatly reduces the annotation effort and leads to the creation of a large-scale Re-ID benchmark called SYSU-30$k$. The new benchmark contains $30k$ individuals, which is about $20$ times larger than CUHK03 ($1.3k$ individuals) and Market-1501 ($1.5k$ individuals), and $30$ times larger than ImageNet ($1k$ categories). It sums up to 29,606,918 images. Learning a Re-ID model with bag-level annotation is called the weakly supervised Re-ID problem. To solve this problem, we introduce a differentiable graphical model to capture the dependencies from all images in a bag and generate a reliable pseudo label for each person image. The pseudo label is further used to supervise the learning of the Re-ID model. When compared with the fully supervised Re-ID models, our method achieves state-of-the-art performance on SYSU-30$k$ and other datasets. The code, dataset, and pretrained model will be available at \url{https://github.com/wanggrun/SYSU-30k}.
Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure
Authors: Xiaohan Ding, Guiguang Ding, Yuchen Guo, Jungong Han
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6

The redundancy is widely recognized in Convolutional Neural Networks (CNNs),
which enables to remove unimportant filters from convolutional layers so as to
slim the network with acceptable performance drop. Inspired by the linear and
combinational properties of convolution, we seek to make some filters
increasingly close and eventually identical for network slimming. To this end,
we propose Centripetal SGD (C-SGD), a novel optimization method, which can
train several filters to collapse into a single point in the parameter
hyperspace. When the training is completed, the removal of the identical
filters can trim the network with NO performance loss, thus no finetuning is
needed. By doing so, we have partly solved an open problem of constrained
filter pruning on CNNs with complicated structure, where some layers must be
pruned following others. Our experimental results on CIFAR-10 and ImageNet have
justified the effectiveness of C-SGD-based filter pruning. Moreover, we have
provided empirical evidences for the assumption that the redundancy in deep
neural networks helps the convergence of training by showing that a redundant
CNN trained using C-SGD outperforms a normally trained counterpart with the
equivalent width.
A Statistical Investigation of Long Memory in Language and Music
Authors: Alexander Greaves-Tunnell, Zaid Harchaoui
Date: 2019-04-08 00:00:00
Tasks: Language Modelling, Time Series Analysis

Representation and learning of long-range dependencies is a central challenge confronted in modern applications of machine learning to sequence data. Yet despite the prominence of this issue, the basic problem of measuring long-range dependence, either in a given data source or as represented in a trained deep model, remains largely limited to heuristic tools. We contribute a statistical framework for investigating long-range dependence in current applications of deep sequence modeling, drawing on the well-developed theory of long memory stochastic processes. This framework yields testable implications concerning the relationship between long memory in real-world data and its learned representation in a deep learning architecture, which are explored through a semiparametric framework adapted to the high-dimensional setting.
Weakly Supervised Person Re-Identification
Authors: Jingke Meng, Sheng Wu, Wei-Shi Zheng
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Multi-Label Learning, Person Re-Identification

In the conventional person re-id setting, it is assumed that the labeled images are the person images within the bounding box for each individual; this labeling across multiple nonoverlapping camera views from raw video surveillance is costly and time-consuming. To overcome this difficulty, we consider weakly supervised person re-id modeling. The weak setting refers to matching a target person with an untrimmed gallery video where we only know that the identity appears in the video without the requirement of annotating the identity in any frame of the video during the training procedure. Hence, for a video, there could be multiple video-level labels. We cast this weakly supervised person re-id challenge into a multi-instance multi-label learning (MIML) problem. In particular, we develop a Cross-View MIML (CV-MIML) method that is able to explore potential intraclass person images from all the camera views by incorporating the intra-bag alignment and the cross-view bag alignment. Finally, the CV-MIML method is embedded into an existing deep neural network for developing the Deep Cross-View MIML (Deep CV-MIML) model. We have performed extensive experiments to show the feasibility of the proposed weakly supervised setting and verify the effectiveness of our method compared to related methods on four weakly labeled datasets.
Ensemble Teaching for Hybrid Label Propagation
Authors: Chen Gong, DaCheng Tao, Xiaojun Chang, Jian Yang
Date: 2019-04-08 00:00:00

Label propagation aims to iteratively diffuse the label information from
labeled examples to unlabeled examples over a similarity graph. Current label
propagation algorithms cannot consistently yield satisfactory performance due
to two reasons: one is the instability of single propagation method in dealing
with various practical data, and the other one is the improper propagation
sequence ignoring the labeling difficulties of different examples. To remedy
above defects, this paper proposes a novel propagation algorithm called hybrid
diffusion under ensemble teaching (HyDEnT). Specifically, HyDEnT integrates
multiple propagation methods as base learners to fully exploit their individual
wisdom, which helps HyDEnT to be stable and obtain consistent encouraging
results. More importantly, HyDEnT conducts propagation under the guidance of an
ensemble of teachers. That is to say, in every propagation round the simplest
curriculum examples are wisely designated by a teaching algorithm, so that
their labels can be reliably and accurately decided by the learners. To
optimally choose these simplest examples, every teacher in the ensemble should
comprehensively consider the examples' difficulties from its own viewpoint, as
well as the common knowledge shared by all the teachers. This is accomplished
by a designed optimization problem, which can be efficiently solved via the
block coordinate descent method. Thanks to the efforts of the teachers, all the
unlabeled examples are logically propagated from simple to difficult, leading
to better propagation quality of HyDEnT than the existing methods.
Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning
Authors: Inseok Oh, Seungeun Rho, Sangbin Moon, Seongho Son, Hyoil Lee, Jinyun Chung
Date: 2019-04-08 00:00:00
Tasks: Board Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning combined with deep neural networks has performed remarkably well in many genres of games recently. It has surpassed human-level performance in fixed game environments and turn-based two player board games. However, to the best of our knowledge, current research has yet to produce a result that has surpassed human-level performance in modern complex fighting games. This is due to the inherent difficulties with real-time fighting games, including: vast action spaces, action dependencies, and imperfect information. We overcame these challenges and made 1v1 battle AI agents for the commercial game "Blade & Soul". The trained agents competed against five professional gamers and achieved a win rate of 62%. This paper presents a practical reinforcement learning method that includes a novel self-play curriculum and data skipping techniques. Through the curriculum, three different styles of agents were created by reward shaping and were trained against each other. Additionally, this paper suggests data skipping techniques that could increase data efficiency and facilitate explorations in vast spaces. Since our method can be generally applied to all two-player competitive games with vast action spaces, we anticipate its application to game development including level design and automated balancing.
WeNet: Weighted Networks for Recurrent Network Architecture Search
Authors: Zhiheng Huang, Bing Xiang
Date: 2019-04-08 00:00:00
Tasks: General Classification, image-classification, Image Classification, Language Modeling, Language Modelling, Mixture-of-Experts, Neural Architecture Search

In recent years, there has been increasing demand for automatic architecture
search in deep learning. Numerous approaches have been proposed and led to
state-of-the-art results in various applications, including image
classification and language modeling. In this paper, we propose a novel way of
architecture search by means of weighted networks (WeNet), which consist of a
number of networks, with each assigned a weight. These weights are updated with
back-propagation to reflect the importance of different networks. Such weighted
networks bear similarity to mixture of experts. We conduct experiments on Penn
Treebank and WikiText-2. We show that the proposed WeNet can find recurrent
architectures which result in state-of-the-art performance.
Towards Real-Time Automatic Portrait Matting on Mobile Devices
Authors: Seokjun Seo, Seungwoo Choi, Martin Kersner, Beomjun Shin, Hyungsuk Yoon, Hyeongmin Byun, Sungjoo Ha
Date: 2019-04-08 00:00:00
Tasks: Image Matting

We tackle the problem of automatic portrait matting on mobile devices. The
proposed model is aimed at attaining real-time inference on mobile devices with
minimal degradation of model performance. Our model MMNet, based on
multi-branch dilated convolution with linear bottleneck blocks, outperforms the
state-of-the-art model and is orders of magnitude faster. The model can be
accelerated four times to attain 30 FPS on Xiaomi Mi 5 device with moderate
increase in the gradient error. Under the same conditions, our model has an
order of magnitude less number of parameters and is faster than Mobile
DeepLabv3 while maintaining comparable performance. The accompanied
implementation can be found at \url{https://github.com/hyperconnect/MMNet}.
Temporal Convolution for Real-time Keyword Spotting on Mobile Devices
Authors: Seungwoo Choi, Seokjun Seo, Beomjun Shin, Hyeongmin Byun, Martin Kersner, Beomsu Kim, Dongyoung Kim, Sungjoo Ha
Date: 2019-04-08 00:00:00
Tasks: Keyword Spotting

Keyword spotting (KWS) plays a critical role in enabling speech-based user interactions on smart devices. Recent developments in the field of deep learning have led to wide adoption of convolutional neural networks (CNNs) in KWS systems due to their exceptional accuracy and robustness. The main challenge faced by KWS systems is the trade-off between high accuracy and low latency. Unfortunately, there has been little quantitative analysis of the actual latency of KWS models on mobile devices. This is especially concerning since conventional convolution-based KWS approaches are known to require a large number of operations to attain an adequate level of performance. In this paper, we propose a temporal convolution for real-time KWS on mobile devices. Unlike most of the 2D convolution-based KWS approaches that require a deep architecture to fully capture both low- and high-frequency domains, we exploit temporal convolutions with a compact ResNet architecture. In Google Speech Command Dataset, we achieve more than \textbf{385x} speedup on Google Pixel 1 and surpass the accuracy compared to the state-of-the-art model. In addition, we release the implementation of the proposed and the baseline models including an end-to-end pipeline for training models and evaluating them on mobile devices.
Binary matrix completion with nonconvex regularizers
Authors: Chunsheng Liu
Date: 2019-04-08 00:00:00
Tasks: Matrix Completion

Many practical problems involve the recovery of a binary matrix from partial
information, which makes the binary matrix completion (BMC) technique received
increasing attention in machine learning. In particular, we consider a special
case of BMC problem, in which only a subset of positive elements can be
observed. In recent years, convex regularization based methods are the
mainstream approaches for this task. However, the applications of nonconvex
surrogates in standard matrix completion have demonstrated better empirical
performance. Accordingly, we propose a novel BMC model with nonconvex
regularizers and provide the recovery guarantee for the model. Furthermore, for
solving the resultant nonconvex optimization problem, we improve the popular
proximal algorithm with acceleration strategies. It can be guaranteed that the
convergence rate of the algorithm is in the order of ${1/T}$, where $T$ is the
number of iterations. Extensive experiments conducted on both synthetic and
real-world data sets demonstrate the superiority of the proposed approach over
other competing methods.
Visual Localization Using Sparse Semantic 3D Map
Authors: Tianxin Shi, Shuhan Shen, Xiang Gao, Lingjie Zhu
Date: 2019-04-08 00:00:00
Tasks: Visual Localization

Accurate and robust visual localization under a wide range of viewing condition variations including season and illumination changes, as well as weather and day-night variations, is the key component for many computer vision and robotics applications. Under these conditions, most traditional methods would fail to locate the camera. In this paper we present a visual localization algorithm that combines structure-based method and image-based method with semantic information. Given semantic information about the query and database images, the retrieved images are scored according to the semantic consistency of the 3D model and the query image. Then the semantic matching score is used as weight for RANSAC's sampling and the pose is solved by a standard PnP solver. Experiments on the challenging long-term visual localization benchmark dataset demonstrate that our method has significant improvement compared with the state-of-the-arts.