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Going deeper with convolutions
TLDR
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). Expand
SSD: Single Shot MultiBox Detector
TLDR
This paper presents the first deep network based object detector that does not resample pixels or features for bounding box hypotheses and and is as accurate as approaches that do. Expand
Explaining and Harnessing Adversarial Examples
TLDR
A simple and fast method of generating adversarial examples that makes adversarial training practical. Expand
Rethinking the Inception Architecture for Computer Vision
TLDR
Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Expand
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
TLDR
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. Expand
Intriguing properties of neural networks
TLDR
We find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. Expand
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
TLDR
We study the combination of the two most recent ideas: Residual connections introduced by He et al. in [5] and the latest revised version of the Inception architecture. Expand
DeepPose: Human Pose Estimation via Deep Neural Networks
TLDR
We propose a method for human pose estimation based on Deep Neural Networks (DNNs). Expand
Training Deep Neural Networks on Noisy Labels with Bootstrapping
TLDR
We propose a generic way to handle noisy and incomplete labeling in weaklysupervised deep learning by augmenting the prediction objective with a notion of consistency. Expand
Scalable Object Detection Using Deep Neural Networks
TLDR
We propose a saliency-inspired neural network model for detection, which predicts a set of class-agnostic bounding boxes along with a single score for each box, corresponding to its likelihood of containing any object of interest. Expand
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