Deep Convolutional Dictionary Learning for Image Denoising

What Have We Learned From Deep Representations for …

what these powerful models actually have learned. In this paper we shed light on deep spatiotemporal net-works by visualizing what excites the learned models us-ing activation maximization by backpropagating on the in-put. We are the first to visualize the hierarchical features

  What, Have, Learned, What we have learned, Have learned

Finding Tiny Faces in the Wild With Generative Adversarial ...

faces, which are unfriendly for the face classifier. Toward-s this end, we design a refinement sub-network to recover some detailed information. In the discriminator network, the basic GAN [17, 12, 8] is trained to distinguish the real and fake high resolution images. To classify faces or non-

  Faces

Squeeze-and-Excitation Networks - openaccess.thecvf.com

Squeeze-and-Excitation Networks Jie Hu1∗ Li Shen2∗ Gang Sun1 hujie@momenta.ai lishen@robots.ox.ac.uk sungang@momenta.ai 1 Momenta 2 Department of Engineering Science, University of Oxford Abstract Convolutional neural networks are built upon the con-

  Network, Excitation, Squeeze and excitation networks, Squeeze

RegularFace: Deep Face Recognition via Exclusive ...

RegularFace: Deep Face Recognition via Exclusive Regularization Kai Zhao Jingyi Xu Ming-Ming Cheng ∗ TKLNDST, CS, Nankai University kaiz.xyz@gmail.com cmm@nankai.edu.cn

  Zhao

Protecting World Leaders Against Deep Fakes

Protecting World Leaders Against Deep Fakes Shruti Agarwal and Hany Farid University of California, Berkeley Berkeley CA, USA {shrutiagarwal, hfarid}@berkeley.edu

Auto-DeepLab: Hierarchical Neural Architecture Search for ...

Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation Chenxi Liu1∗, Liang-Chieh Chen 2, Florian Schroff2, Hartwig Adam2, Wei Hua2, Alan Yuille1, Li Fei-Fei3 1Johns Hopkins University 2Google 3Stanford University Abstract Recently, NeuralArchitectureSearch(NAS)hassuccess-

PointNet: Deep Learning on Point Sets ... - CVF Open Access

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Charles R. Qi* Hao Su* Kaichun Mo Leonidas J. Guibas Stanford University

  Open, Learning, Points, Deep, Sets, Pointnet, Deep learning on point sets

Frustum PointNets for 3D Object Detection From RGB-D Data

Frustum PointNets for 3D Object Detection from RGB-D Data Charles R. Qi1∗ Wei Liu2 Chenxia Wu2 Hao Su3 Leonidas J. Guibas1 1Stanford University 2Nuro, Inc. 3UC San Diego Abstract In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.

Class-Balanced Loss Based on Effective Number of Samples

and large-scale datasets including ImageNet and iNatural-ist. Our results show that when trained with the proposed class-balanced loss, the network is able to achieve signifi-cant performance gains on long-tailed datasets. 1. Introduction The recent success of deep Convolutional Neural Net-works (CNNs) for visual recognition [26, 37, 38, 16] owes

  Scale, Visual, Recognition, Visual recognition

ESRGAN: Enhanced Super-Resolution Generative Adversarial ...

ESRGAN: EnhancedSuper-Resolution Generative Adversarial Networks Xintao Wang 1, Ke Yu , Shixiang Wu2, Jinjin Gu3, Yihao Liu4, Chao Dong 2, Yu Qiao , and Chen Change Loy5 1 CUHK-SenseTime Joint Lab, The Chinese University of Hong Kong 2 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 3 The Chinese University of Hong Kong, …

  Network, Adversarial, Generative, Generative adversarial, Generative adversarial networks

arXiv:1706.02216v4 [cs.SI] 10 Sep 2018

Graph convolutional networks. In recent years, several convolutional neural network architectures for learning over graphs have been proposed (e.g., [4, 9, 8, 17, 24]). The majority of these methods do not scale to large graphs or are designed for whole-graph classification (or both) [4, 9, 8, 24].

  Convolutional

Abstract - arXiv

Convolutional LSTM Network: A Machine Learning ... According to the philosophy underlying the deep learning approach, if we have a reasonable end-to-end model and sufficient data for training it, we are close to solving the ... The pioneering LSTM encoder-decoder framework proposed in [23] provides a

  Decoder, Deep, Convolutional

Image Colorization with Deep Convolutional Neural …

Image Colorization with Deep Convolutional Neural Networks Jeff Hwang jhwang89@stanford.edu You Zhou youzhou@stanford.edu Abstract We present a convolutional-neural-network-based sys-tem that faithfully colorizes black and white photographic images without direct human assistance. We explore var-ious network architectures, objectives, color ...

  Network, With, Image, Deep, Neural, Convolutional, Colorization, Image colorization with deep convolutional neural, Image colorization with deep convolutional neural networks

Convolutional Neural Networks

Convolutional Neural Networks Lingxiao Yang 1 2 3Ru-Yuan Zhang4 5 Lida Li6 Xiaohua Xie Abstract ... s are based on a hand-crafted encoder-decoder structure. Compared to that study, our work provides an alternative ... Network Architectures. In 2012, a modern deep ConvNet, AlexNet (Krizhevsky et al.,2012), was released for large-scale image ...

  Decoder, Deep, Convolutional

Stacked Convolutional Auto-Encoders for Hierarchical ...

Deep architectures can be trained in an unsupervised layer-wise fashion, and later fine-tuned by back-propagationto be-come classifiers [9]. Unsupervised initializations tend to avoid local minima and increase the network’s performance stability [6]. Most methods are based on the encoder-decoder paradigm, e.g., [20]. The in-

  Decoder, Deep, Convolutional

Long-Term Recurrent Convolutional Networks for Visual ...

deep”, are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image de-scription and retrieval problems, and video narration ...

  Deep, Convolutional

Convolutional LSTM Network: A Machine Learning Approach ...

Convolutional LSTM Network: A Machine Learning ... According to the philosophy underlying the deep learning approach, if we have a reasonable end-to-end model and sufficient data for training it, we are close to solving the ... The pioneering LSTM encoder-decoder framework proposed in [23] provides a

  Decoder, Deep, Convolutional

Image Restoration Using Very Deep Convolutional Encoder ...

The proposed framework mainly contains a chain of convolutional layers and symmetric decon-volutional layers, as shown in Figure 1. We term our method “RED-Net”—very deep Residual Encoder-Decoder Networks. 2.1 Architecture The framework is fully convolutional and deconvolutional. Rectification layers are added after each

  Using, Decoder, Deep, Restoration, Very, Convolutional, Restoration using very deep convolutional

Graph Convolutional Matrix Completion

The decoder model is a pairwise decoder Aˇ = д(Z), which takes pairs of node embeddings (zi,zj)and predicts entries Aˇ ... Graph Convolutional Matrix Completion KDD’18 Deep Learning Day, August 2018, London, UK.

  Decoder, Matrix, Deep, Completion, Graph, Convolutional, Graph convolutional matrix completion