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Visualizing Data using t-SNE
We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of StochasticExpand
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Dimensionality Reduction: A Comparative Review
In recent years, a variety of nonlinear dimensionality reduction techniques have been proposed that aim to address the limitations of traditional techniques such as PCA and classical scaling. TheExpand
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Accelerating t-SNE using tree-based algorithms
The paper investigates the acceleration of t-SNE--an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots--using two tree-based algorithms. InExpand
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Countering Adversarial Images using Input Transformations
This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system. Specifically, we studyExpand
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Inferring and Executing Programs for Visual Reasoning
Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes. As a result, theseExpand
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Feature Denoising for Improving Adversarial Robustness
Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on imagesExpand
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Exploring the Limits of Weakly Supervised Pretraining
State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining. ImageNet classification is the de facto pretraining task for these models. Yet, ImageNet is nowExpand
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3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (e.g., photos), many other dataExpand
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CondenseNet: An Efficient DenseNet Using Learned Group Convolutions
Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architecture with unprecedentedExpand
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Stochastic triplet embedding
This paper considers the problem of learning an embedding of data based on similarity triplets of the form “A is more similar to B than to C”. This learning setting is of relevance to scenarios inExpand
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