Jonathon Shlens

Publications96
h-index43
Citations43,728
Highly Influential Citations7,045
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  • Publications
  • Influence
Explaining and Harnessing Adversarial Examples
TLDR
A simple and fast method of generating adversarial examples that makes adversarial training practical. Expand
  • 6,988
  • 2045
  • PDF
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
  • 11,004
  • 1698
  • PDF
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TLDR
This paper describes the TensorFlow interface for expressing machine learning algorithms, and an implementation of that interface that we have built at Google. Expand
  • 8,226
  • 926
  • PDF
Learning Transferable Architectures for Scalable Image Recognition
TLDR
We study a method to learn the model architectures directly on the dataset of interest and apply them to ImageNet. Expand
  • 2,428
  • 483
  • PDF
Conditional Image Synthesis with Auxiliary Classifier GANs
TLDR
In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. Expand
  • 1,632
  • 301
  • PDF
DeViSE: A Deep Visual-Semantic Embedding Model
TLDR
We present a new deep visual-semantic embedding model trained to identify visual objects using both labeled image data as well as semantic information gleaned from unannotated text. Expand
  • 1,577
  • 196
  • PDF
A Tutorial on Principal Component Analysis
TLDR
Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but (sometimes) poorly understood. Expand
  • 1,710
  • 180
  • PDF
Adversarial Autoencoders
TLDR
We propose an adversarial autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector with an arbitrary prior distribution. Expand
  • 1,115
  • 175
  • PDF
Progressive Neural Architecture Search
TLDR
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms. Expand
  • 941
  • 169
  • PDF
Zero-Shot Learning by Convex Combination of Semantic Embeddings
TLDR
We propose a simple method for constructing an image embedding system from any existing \nway{} image classifier and a semantic word embedding model, which contains the $\n$ class labels in its vocabulary. Expand
  • 644
  • 120
  • PDF
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