Deep Residual Learning for Image Recognition - …

Deep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research fkahe, v-xiangz, v-shren, jiansung@microsoft.com

  Image, Learning, Residual, Recognition, Residual learning for image recognition

Adversarial Generative Nets: Neural Network …

Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer Carnegie Mellon University

  Network, Attacks, Nets, Adversarial generative nets, Adversarial, Generative, Neural network, Neural, Neural network attacks

arXiv:0706.3639v1 [cs.AI] 25 Jun 2007

arXiv:0706.3639v1 [cs.AI] 25 Jun 2007 Technical Report IDSIA-07-07 A Collection of Definitions of Intelligence Shane Legg IDSIA, Galleria …

  Intelligence, Collection

arXiv:1301.3781v3 [cs.CL] 7 Sep 2013

For all the following models, the training complexity is proportional to O = E T Q; (1) where E is number of the training epochs, T is the number of …

@google.com arXiv:1609.03499v2 [cs.SD] 19 Sep 2016

where 1 <x t <1 and = 255. This non-linear quantization produces a significantly better reconstruction than a simple linear quantization scheme. …

A Tutorial on UAVs for Wireless Networks: …

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems Mohammad Mozaffari 1, ... to UAVs in wireless communications is the work in …

  Network, Communication, Wireless, Wireless communications, Wireless networks

Massive Exploration of Neural Machine Translation ...

Massive Exploration of Neural Machine Translation Architectures Denny Britzy, Anna Goldie, Minh-Thang Luong, Quoc Le fdennybritz,agoldie,thangluong,qvlg@google.com Google Brain

  Architecture, Machine, Exploration, Translation, Neural, Exploration of neural machine translation, Exploration of neural machine translation architectures

Mastering Chess and Shogi by Self-Play with a …

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm David Silver, 1Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, 1Matthew Lai, Arthur Guez, Marc Lanctot,1

Going deeper with convolutions - arXiv

Going deeper with convolutions Christian Szegedy Google Inc. Wei Liu University of North Carolina, Chapel Hill Yangqing Jia Google Inc. Pierre Sermanet

  With, Going, Going deeper with convolutions, Deeper, Convolutions

Andrew G. Howard Menglong Zhu Bo Chen Dmitry ...

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto Hartwig Adam

  Applications

Deep Learning for Generic Object Detection: A Survey

12 Visual object recognition Grauman and Leibe (2011) 2011 Tutorial Instance and category object recognition techniques 13 Object class detection: a survey Zhang et al. (2013) 2013 ACM CS Survey of generic object detection methods before 2011 14 Feature representation for

  Technique, Recognition, Object, Object recognition, Object recognition techniques

Selective Search for Object Recognition

techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The Selective Search software is made publicly available 1. 1 Introduction For a long time, objects were sought to be delineated before their identification.

  Search, Selective, Technique, Recognition, Object, Selective search for object recognition, For object recognition, Selective search

CNNs for Face Detection and Recognition

development of object classification, localization and detec-tion techniques. 2.1. Sliding Window In the early development of face detection, researchers tended to treat it as a repetitive task of object classifica-tion, by imposing sliding windows and performing object classification with the neural networks on the window re-gion.

  Technique, Faces, Recognition, Object, Detection, For face detection and recognition

Theory of Cognitive Pattern Recognition

human perception of the size of object keeps constant. The size of image on the human Open Access Database www.i-techonline.com retina directly depends on the distan ce between the object and our eyes. Source: Pattern Recognition Techniques, Technology and Applications, Book edited by: Peng-Yeng Yin,

  Technique, Theory, Cognitive, Recognition, Object, Patterns, Theory of cognitive pattern recognition, Recognition techniques

Selective Search for Object Recognition

Hence without prior recognition it is hard to decide that a face and a sweater are part of one object ( Tu et al. 2005). This has led to the opposite of the traditional approach: to do localisation through the identification of an object. This recent approach in object recognition has made enor-mousprogressinlessthanadecade(DalalandTriggs2005;

  Recognition, Object, Object recognition, For object recognition

Bilinear CNN Models for Fine-grained Visual Recognition

Fine-grained recognition tasks such as identifying the species of a bird, or the model of an aircraft, are quite challenging because the visual differences between the cat-egories are small and can be easily overwhelmed by those causedbyfactorssuchaspose,viewpoint,orlocationofthe object in the image. For example, the inter-category vari-

  Fine, Recognition, Object

You Only Look Once: Unified, Real-Time Object Detection

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to per-form detection. Instead, we frame object detection as a re-gression problem to spatially separated bounding boxes and associated class probabilities. A single neural network pre-dicts bounding boxes and class probabilities directly ...

  Object, Yolo