Variational VQA applies variational Bayes to improve calibration and selective prediction on VQA and visual reasoning tasks, with gains at low error tolerance via a risk-averse selector that uses prediction variance.
Overpruning in Variational Bayesian Neural Networks
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abstract
The motivations for using variational inference (VI) in neural networks differ significantly from those in latent variable models. This has a counter-intuitive consequence; more expressive variational approximations can provide significantly worse predictions as compared to those with less expressive families. In this work we make two contributions. First, we identify a cause of this performance gap, variational over-pruning. Second, we introduce a theoretically grounded explanation for this phenomenon. Our perspective sheds light on several related published results and provides intuition into the design of effective variational approximations of neural networks.
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cs.CV 1years
2025 1verdicts
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Variational Visual Question Answering for Uncertainty-Aware Selective Prediction
Variational VQA applies variational Bayes to improve calibration and selective prediction on VQA and visual reasoning tasks, with gains at low error tolerance via a risk-averse selector that uses prediction variance.