Search Results for author: Daniel Keysers

Found 16 papers, 8 papers with code

Video OWL-ViT: Temporally-consistent open-world localization in video

no code implementations ICCV 2023 Georg Heigold, Matthias Minderer, Alexey Gritsenko, Alex Bewley, Daniel Keysers, Mario Lučić, Fisher Yu, Thomas Kipf

Our model is end-to-end trainable on video data and enjoys improved temporal consistency compared to tracking-by-detection baselines, while retaining the open-world capabilities of the backbone detector.

Object Object Localization

LiT: Zero-Shot Transfer with Locked-image text Tuning

4 code implementations CVPR 2022 Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, Lucas Beyer

This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text models while still taking advantage of their pre-training.

Image Classification Retrieval +2

The Impact of Reinitialization on Generalization in Convolutional Neural Networks

no code implementations1 Sep 2021 Ibrahim Alabdulmohsin, Hartmut Maennel, Daniel Keysers

Recent results suggest that reinitializing a subset of the parameters of a neural network during training can improve generalization, particularly for small training sets.

Generalization Bounds Image Classification +1

A Generalized Lottery Ticket Hypothesis

no code implementations3 Jul 2021 Ibrahim Alabdulmohsin, Larisa Markeeva, Daniel Keysers, Ilya Tolstikhin

We introduce a generalization to the lottery ticket hypothesis in which the notion of "sparsity" is relaxed by choosing an arbitrary basis in the space of parameters.

What Do Neural Networks Learn When Trained With Random Labels?

no code implementations NeurIPS 2020 Hartmut Maennel, Ibrahim Alabdulmohsin, Ilya Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers

We show how this alignment produces a positive transfer: networks pre-trained with random labels train faster downstream compared to training from scratch even after accounting for simple effects, such as weight scaling.


Predicting Neural Network Accuracy from Weights

1 code implementation26 Feb 2020 Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, Ilya Tolstikhin

Furthermore, the predictors are able to rank networks trained on different, unobserved datasets and with different architectures.

Measuring Compositional Generalization: A Comprehensive Method on Realistic Data

3 code implementations ICLR 2020 Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, Olivier Bousquet

We present a large and realistic natural language question answering dataset that is constructed according to this method, and we use it to analyze the compositional generalization ability of three machine learning architectures.

BIG-bench Machine Learning Question Answering +1

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