30 research outputs found
Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation
Deep neural networks are highly susceptible to learning biases in visual
data. While various methods have been proposed to mitigate such bias, the
majority require explicit knowledge of the biases present in the training data
in order to mitigate. We argue the relevance of exploring methods which are
completely ignorant of the presence of any bias, but are capable of identifying
and mitigating them. Furthermore, we propose using Bayesian neural networks
with an epistemic uncertainty-weighted loss function to dynamically identify
potential bias in individual training samples and to weight them during
training. We find a positive correlation between samples subject to bias and
higher epistemic uncertainties. Finally, we show the method has potential to
mitigate visual bias on a bias benchmark dataset and on a real-world face
detection problem, and we consider the merits and weaknesses of our approach.Comment: To be published in 2022 IEEE CVPR Workshop on Fair, Data Efficient
and Trusted Computer Visio
Bayesian uncertainty-weighted loss for improved generalisability on polyp segmentation task
While several previous studies have devised methods for segmentation of
polyps, most of these methods are not rigorously assessed on multi-center
datasets. Variability due to appearance of polyps from one center to another,
difference in endoscopic instrument grades, and acquisition quality result in
methods with good performance on in-distribution test data, and poor
performance on out-of-distribution or underrepresented samples. Unfair models
have serious implications and pose a critical challenge to clinical
applications. We adapt an implicit bias mitigation method which leverages
Bayesian epistemic uncertainties during training to encourage the model to
focus on underrepresented sample regions. We demonstrate the potential of this
approach to improve generalisability without sacrificing state-of-the-art
performance on a challenging multi-center polyp segmentation dataset (PolypGen)
with different centers and image modalities.Comment: To be presented at the Fairness of AI in Medical Imaging (FAIMI)
MICCAI 2023 Workshop and published in volumes of the Springer Lecture Notes
Computer Science (LNCS) serie
Learning Spatio-Temporal Patterns for Predicting Object Behaviour
Rule-based systems employed to model complex object behaviours, do not necessarily provide a realistic portrayal of true behaviour. To capture the real characteristics in a specific environment, a better model may be learnt from observation. This paper presents a novel approach to learning long-term spatio-temporal patterns of objects in image sequences, using a neural network paradigm to predict future behaviour. The results demonstrate the application of our approach to the problem of predicting animal behaviour in response to a predator