171 citations · 694 across the 32 of their papers we have counts for
38 papers
Unsupervised Video Prediction from a Single Frame by Estimating 3D Dynamic Scene Structure
Paul Henderson, Christoph H. Lampert, Bernd Bickel
Our goal in this work is to generate realistic videos given just one initial frame as input. Existing unsupervised approaches to this task do not consider the fact that a video typ…
Towards Understanding Knowledge Distillation
Mary Phuong, Christoph H. Lampert
Knowledge distillation, i.e., one classifier being trained on the outputs of another classifier, is an empirically very successful technique for knowledge transfer between classifi…
Fairness Through Regularization for Learning to Rank
Nikola Konstantinov, Christoph H. Lampert
Given the abundance of applications of ranking in recent years, addressing fairness concerns around automated ranking systems becomes necessary for increasing the trust among end-u…
A Flexible Selection Scheme for Minimum-Effort Transfer Learning
Amelie Royer, Christoph H. Lampert
Fine-tuning is a popular way of exploiting knowledge contained in a pre-trained convolutional network for a new visual recognition task. However, the orthogonal setting of transfer…
Unsupervised object-centric video generation and decomposition in 3D
Paul Henderson, Christoph H. Lampert
A natural approach to generative modeling of videos is to represent them as a composition of moving objects. Recent works model a set of 2D sprites over a slowly-varying background…
Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios
Amelie Royer, Christoph H. Lampert
State-of-the-art detection systems are generally evaluated on their ability to exhaustively retrieve objects densely distributed in the image, across a wide variety of appearances…
Leveraging 2D Data to Learn Textured 3D Mesh Generation
Paul Henderson, Vagia Tsiminaki, Christoph H. Lampert
Numerous methods have been proposed for probabilistic generative modelling of 3D objects. However, none of these is able to produce textured objects, which renders them of limited…
Object-Centric Image Generation with Factored Depths, Locations, and Appearances
Titas Anciukevicius, Christoph H. Lampert, Paul Henderson
We present a generative model of images that explicitly reasons over the set of objects they show. Our model learns a structured latent representation that separates objects from e…
On the Sample Complexity of Adversarial Multi-Source PAC Learning
Nikola Konstantinov, Elias Frantar, Dan Alistarh +1
We study the problem of learning from multiple untrusted data sources, a scenario of increasing practical relevance given the recent emergence of crowdsourcing and collaborative le…
Strategy Representation by Decision Trees with Linear Classifiers
Pranav Ashok, Tomáš Brázdil, Krishnendu Chatterjee +3
Graph games and Markov decision processes (MDPs) are standard models in reactive synthesis and verification of probabilistic systems with nondeterminism. The class of -regular w…
Robust Learning from Untrusted Sources
Nikola Konstantinov, Christoph Lampert
Modern machine learning methods often require more data for training than a single expert can provide. Therefore, it has become a standard procedure to collect data from external s…
Back to square one: probabilistic trajectory forecasting without bells and whistles
Ehsan Pajouheshgar, Christoph H. Lampert
We introduce a spatio-temporal convolutional neural network model for trajectory forecasting from visual sources. Applied in an auto-regressive way it provides an explicit probabil…
Dependency-dependent Bounds for Sums of Dependent Random Variables
Christoph H. Lampert, Liva Ralaivola, Alexander Zimin
We consider the problem of bounding large deviations for non-i.i.d. random variables that are allowed to have arbitrary dependencies. Previous works typically assumed a specific de…
Detecting Visual Relationships Using Box Attention
Alexander Kolesnikov, Alina Kuznetsova, Christoph H. Lampert +1
We propose a new model for detecting visual relationships, such as "person riding motorcycle" or "bottle on table". This task is an important step towards comprehensive structured…
Learning Equations for Extrapolation and Control
Subham S. Sahoo, Christoph H. Lampert, Georg Martius
We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understan…
KS(conf ): A Light-Weight Test if a ConvNet Operates Outside of Its Specifications
Rémy Sun, Christoph H. Lampert
Computer vision systems for automatic image categorization have become accurate and reliable enough that they can run continuously for days or even years as components of real-worl…
MACRO: A Meta-Algorithm for Conditional Risk Minimization
Alexander Zimin, Christoph Lampert
We study conditional risk minimization (CRM), i.e. the problem of learning a hypothesis of minimal risk for prediction at the next step of sequentially arriving dependent data. Des…
Learning Intelligent Dialogs for Bounding Box Annotation
Ksenia Konyushkova, Jasper Uijlings, Christoph Lampert +1
We introduce Intelligent Annotation Dialogs for bounding box annotation. We train an agent to automatically choose a sequence of actions for a human annotator to produce a bounding…
Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly
Yongqin Xian, Christoph H. Lampert, Bernt Schiele +1
Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadi…
Probabilistic Image Colorization
Amelie Royer, Alexander Kolesnikov, Christoph H. Lampert
We develop a probabilistic technique for colorizing grayscale natural images. In light of the intrinsic uncertainty of this task, the proposed probabilistic framework has numerous…
Data-Dependent Stability of Stochastic Gradient Descent
Ilja Kuzborskij, Christoph H. Lampert
We establish a data-dependent notion of algorithmic stability for Stochastic Gradient Descent (SGD), and employ it to develop novel generalization bounds. This is in contrast to pr…
PixelCNN Models with Auxiliary Variables for Natural Image Modeling
Alexander Kolesnikov, Christoph H. Lampert
We study probabilistic models of natural images and extend the autoregressive family of PixelCNN architectures by incorporating auxiliary variables. Subsequently, we describe two n…
iCaRL: Incremental Classifier and Representation Learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl +1
A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of…
Extrapolation and learning equations
Georg Martius, Christoph H. Lampert
In classical machine learning, regression is treated as a black box process of identifying a suitable function from a hypothesis set without attempting to gain insight into the mec…