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Explaining and Harnessing Adversarial Examples
TLDR
It is argued that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature, supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets.
Rethinking the Inception Architecture for Computer Vision
TLDR
This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TLDR
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.
Learning Transferable Architectures for Scalable Image Recognition
TLDR
This paper proposes to search for an architectural building block on a small dataset and then transfer the block to a larger dataset and introduces a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models.
Conditional Image Synthesis with Auxiliary Classifier GANs
TLDR
A variant of GANs employing label conditioning that results in 128 x 128 resolution image samples exhibiting global coherence is constructed and it is demonstrated that high resolution samples provide class information not present in low resolution samples.
DeViSE: A Deep Visual-Semantic Embedding Model
TLDR
This paper presents 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 and shows that the semantic information can be exploited to make predictions about tens of thousands of image labels not observed during training.
Progressive Neural Architecture Search
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
Adversarial Autoencoders
TLDR
This paper shows how the adversarial autoencoder can be used in applications such as semi-supervised classification, disentangling style and content of images, unsupervised clustering, dimensionality reduction and data visualization, and performed experiments on MNIST, Street View House Numbers and Toronto Face datasets.
A Tutorial on Principal Component Analysis
TLDR
This manuscript focuses on building a solid intuition for how and why principal component analysis works, and crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA.
Randaugment: Practical automated data augmentation with a reduced search space
TLDR
This work proposes a simplified search space that vastly reduces the computational expense of automated augmentation, and permits the removal of a separate proxy task.
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