Ondrej Bohdal | DeepAI

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∙ 07/20/2023

Label Calibration for Semantic Segmentation Under Domain Shift

Performance of a pre-trained semantic segmentation model is likely to su...

0 Ondrej Bohdal, et al. ∙

∙ 07/20/2023

Feed-Forward Source-Free Domain Adaptation via Class Prototypes

Source-free domain adaptation has become popular because of its practica...

0 Ondrej Bohdal, et al. ∙

∙ 06/30/2023

Impact of Noise on Calibration and Generalisation of Neural Networks

Noise injection and data augmentation strategies have been effective for...

0 Martin Ferianc, et al. ∙

∙ 05/12/2023

Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn

Meta-learning and other approaches to few-shot learning are widely studi...

0 Ondrej Bohdal, et al. ∙

∙ 04/16/2023

Fairness in AI and Its Long-Term Implications on Society

Successful deployment of artificial intelligence (AI) in various setting...

0 Ondrej Bohdal, et al. ∙

∙ 07/15/2022

Feed-Forward Source-Free Latent Domain Adaptation via Cross-Attention

We study the highly practical but comparatively under-studied problem of...

0 Ondrej Bohdal, et al. ∙

∙ 07/14/2022

PASHA: Efficient HPO with Progressive Resource Allocation

Hyperparameter optimization (HPO) and neural architecture search (NAS) a...

0 Ondrej Bohdal, et al. ∙

∙ 07/15/2021

A Channel Coding Benchmark for Meta-Learning

Meta-learning provides a popular and effective family of methods for dat...

0 Rui Li, et al. ∙

∙ 06/19/2021

EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization

Gradient-based meta-learning and hyperparameter optimization have seen s...

0 Ondrej Bohdal, et al. ∙

∙ 06/17/2021

Meta-Calibration: Meta-Learning of Model Calibration Using Differentiable Expected Calibration Error

Calibration of neural networks is a topical problem that is becoming inc...

0 Ondrej Bohdal, et al. ∙

∙ 06/15/2020

Flexible Dataset Distillation: Learn Labels Instead of Images

We study the problem of dataset distillation - creating a small set of s...

0 Ondrej Bohdal, et al. ∙

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