activity
20152021
most citedTowards Understanding Knowledge Distillation

132 citations · 166 across the 6 of their papers we have counts for

collaborators

19 papers

cs.CV2021

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…

cs.LG2021132 cited

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…

cs.LG2021

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…

cs.CV202015 cited

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…

cs.CV2020

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…

cs.CV2020

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…