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Going deeper with convolutions
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 RecognitionExpand
SSD: Single Shot MultiBox Detector
TLDR
The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component. Expand
Intriguing properties of neural networks
TLDR
It is found that there is no distinction between individual highlevel units and random linear combinations of high level units, according to various methods of unit analysis, and it is suggested that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. Expand
Show and tell: A neural image caption generator
TLDR
This paper presents a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. Expand
Theano: A Python framework for fast computation of mathematical expressions
TLDR
The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed. Expand
Challenges in representation learning: A report on three machine learning contests
TLDR
The datasets created for these challenges are described, the results of the competitions are summarized, and some comments are provided on what kind of knowledge can be gained from machine learning competitions. Expand
Domain Separation Networks
TLDR
The novel architecture results in a model that outperforms the state-of-the-art on a range of unsupervised domain adaptation scenarios and additionally produces visualizations of the private and shared representations enabling interpretation of the domain adaptation process. Expand
An empirical evaluation of deep architectures on problems with many factors of variation
TLDR
A series of experiments indicate that these models with deep architectures show promise in solving harder learning problems that exhibit many factors of variation. Expand
Show and Tell: Lessons Learned from the 2015 MSCOCO Image Captioning Challenge
TLDR
A generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image is presented. Expand
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
TLDR
This generative adversarial network (GAN)-based method adapts source-domain images to appear as if drawn from the target domain, and outperforms the state-of-the-art on a number of unsupervised domain adaptation scenarios by large margins. Expand
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