Journal ArticleDOI
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.
TLDR
Fast AnoGAN (f‐AnoGAN), a generative adversarial network (GAN) based unsupervised learning approach capable of identifying anomalous images and image segments, that can serve as imaging biomarker candidates is presented.About:
This article is published in Medical Image Analysis.The article was published on 2019-05-01. It has received 777 citations till now. The article focuses on the topics: Generative model & Supervised learning.read more
Citations
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Journal ArticleDOI
Deep Learning for Anomaly Detection: A Review
TL;DR: A comprehensive survey of deep anomaly detection with a comprehensive taxonomy is presented in this paper, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods.
Journal ArticleDOI
Deep Learning for Anomaly Detection: A Review
TL;DR: This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods and discusses how they address the aforementioned challenges.
Proceedings ArticleDOI
Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings
TL;DR: A powerful student-teacher framework for the challenging problem of unsupervised anomaly detection and pixel-precise anomaly segmentation in high-resolution images by trained to regress the output of a descriptive teacher network that was pretrained on a large dataset of patches from natural images.
Journal ArticleDOI
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff,Jacob R. Kauffmann,Robert A. Vandermeulen,Grégoire Montavon,Wojciech Samek,Marius Kloft,Thomas G. Dietterich,Klaus-Robert Müller +7 more
TL;DR: This review aims to identify the common underlying principles and the assumptions that are often made implicitly by various methods in deep learning, and draws connections between classic “shallow” and novel deep approaches and shows how this relation might cross-fertilize or extend both directions.
Journal ArticleDOI
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff,Jacob R. Kauffmann,Robert A. Vandermeulen,Grégoire Montavon,Wojciech Samek,Marius Kloft,Thomas G. Dietterich,Klaus-Robert Müller +7 more
TL;DR: Deep learning approaches to anomaly detection (AD) have recently improved the state of the art in detection performance on complex data sets, such as large collections of images or text as mentioned in this paper, and led to the introduction of a great variety of new methods.
References
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Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva,Brett Kuprel,Roberto A. Novoa,Justin M. Ko,Susan M. Swetter,Susan M. Swetter,Helen M. Blau,Sebastian Thrun +7 more
TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.
Proceedings ArticleDOI
Extracting and composing robust features with denoising autoencoders
TL;DR: This work introduces and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern.