Search Results for author: Lukas Ruff

Found 16 papers, 6 papers with code

Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides

no code implementations3 Feb 2023 Miriam Hägele, Johannes Eschrich, Lukas Ruff, Maximilian Alber, Simon Schallenberg, Adrien Guillot, Christoph Roderburg, Frank Tacke, Frederick Klauschen

Motivated by the medical application, we demonstrate for general segmentation tasks that including additional patches with solely weak complementary labels during model training can significantly improve the predictive performance and robustness of a model.

Segmentation Semantic Segmentation +1

Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images

1 code implementation23 May 2022 Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft

We find that standard classifiers and semi-supervised one-class methods trained to discern between normal samples and relatively few random natural images are able to outperform the current state of the art on an established AD benchmark with ImageNet.

 Ranked #1 on Anomaly Detection on One-class CIFAR-10 (using extra training data)

Anomaly Detection

Hierarchical Multimodal Variational Autoencoders

no code implementations29 Sep 2021 Jannik Wolff, Rahul G Krishnan, Lukas Ruff, Jan Nikolas Morshuis, Tassilo Klein, Shinichi Nakajima, Moin Nabi

Humans find structure in natural phenomena by absorbing stimuli from multiple input sources such as vision, text, and speech.

Deep Anomaly Detection by Residual Adaptation

no code implementations5 Oct 2020 Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen, Hakan Bilen

Deep anomaly detection is a difficult task since, in high dimensions, it is hard to completely characterize a notion of "differentness" when given only examples of normality.

Anomaly Detection Disentanglement

Geometric Disentanglement by Random Convex Polytopes

no code implementations29 Sep 2020 Michael Joswig, Marek Kaluba, Lukas Ruff

We propose a new geometric method for measuring the quality of representations obtained from deep learning.

Clustering Disentanglement +1

A Unifying Review of Deep and Shallow Anomaly Detection

no code implementations24 Sep 2020 Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller

Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text.

One-Class Classification

Explainable Deep One-Class Classification

2 code implementations ICLR 2021 Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, Klaus-Robert Müller

Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away.

Ranked #5 on Anomaly Detection on One-class ImageNet-30 (using extra training data)

Classification General Classification +2

The Clever Hans Effect in Anomaly Detection

no code implementations18 Jun 2020 Jacob Kauffmann, Lukas Ruff, Grégoire Montavon, Klaus-Robert Müller

The 'Clever Hans' effect occurs when the learned model produces correct predictions based on the 'wrong' features.

Anomaly Detection Explainable Artificial Intelligence (XAI) +1

Rethinking Assumptions in Deep Anomaly Detection

1 code implementation30 May 2020 Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft

Though anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have access to, or it is infeasible to utilize, a dataset that sufficiently characterizes what it means to be "anomalous."

Anomaly Detection

Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification

no code implementations24 Jan 2020 Penny Chong, Lukas Ruff, Marius Kloft, Alexander Binder

However, deep SVDD suffers from hypersphere collapse -- also known as mode collapse, if the architecture of the model does not comply with certain architectural constraints, e. g. the removal of bias terms.

General Classification One-Class Classification

Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text

1 code implementation ACL 2019 Lukas Ruff, Yury Zemlyanskiy, V, Robert ermeulen, Thomas Schnake, Marius Kloft

There exist few text-specific methods for unsupervised anomaly detection, and for those that do exist, none utilize pre-trained models for distributed vector representations of words.

Contextual Anomaly Detection General Classification +3

Deep One-Class Classification

1 code implementation ICML 2018 Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, Marius Kloft

Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection.

Classification One-Class Classification +1

Anomaly Detection with Generative Adversarial Networks

no code implementations ICLR 2018 Lucas Deecke, Robert Vandermeulen, Lukas Ruff, Stephan Mandt, Marius Kloft

Many anomaly detection methods exist that perform well on low-dimensional problems however there is a notable lack of effective methods for high-dimensional spaces, such as images.

Anomaly Detection

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