Skip to search formSkip to main contentSkip to account menu

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 Recognition…
View on IEEE (opens in a new tab)

Caffe: Convolutional Architecture for Fast Feature Embedding

Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
View on ACM (opens in a new tab)

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.
View PDF on arXiv (opens in a new tab)

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

This paper empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization and enable training visual recognition models on internet-scale data with high efficiency.
View PDF on arXiv (opens in a new tab)

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

DeCAF, an open-source implementation of deep convolutional activation features, along with all associated network parameters, are released to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.
View PDF on arXiv (opens in a new tab)

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

This work proposes a differentiable neural architecture search (DNAS) framework that uses gradient-based methods to optimize ConvNet architectures, avoiding enumerating and training individual architectures separately as in previous methods.
View on IEEE (opens in a new tab)

MatchNet: Unifying feature and metric learning for patch-based matching

A unified approach to combining feature computation and similarity networks for training a patch matching system that improves accuracy over previous state-of-the-art results on patch matching datasets, while reducing the storage requirement for descriptors is confirmed.
View on IEEE (opens in a new tab)

Deep Convolutional Ranking for Multilabel Image Annotation

It is shown that a significant performance gain could be obtained by combining convolutional architectures with approximate top-$k$ ranking objectives, as thye naturally fit the multilabel tagging problem.
View PDF on arXiv (opens in a new tab)

A category-level 3-D object dataset: Putting the Kinect to work

A dataset of color and depth image pairs, gathered in real domestic and office environments, establishes baseline performance in a PASCAL VOC-style detection task, and suggests two ways that inferred world size of the object may be used to improve detection.
View on IEEE (opens in a new tab)

Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective

The hardware and software infrastructure that supports machine learning at global scale is described, leveraging both GPU and CPU platforms for training and abundant CPU capacity for real-time inference.
View on IEEE (opens in a new tab)
...
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)