Dec 21, 2021 · In this paper, we propose a new approach to train a deep neural network with multiple intermediate auxiliary classifiers, branching from it.

This paper proposes a way to train jointly all the branches of a multi-exit model without hyper-parameters, by weighting the predictions from each branch ...

In this paper, we propose a new approach to train a deep neural network with multiple intermediate auxiliary classifiers, branching from it.

The authors in the fourth paper [4], titled “A Probabilistic Re-Interpretation of Confidence Scores in Multi-Exit Models”, propose an approach to train a deep ...

In this paper, we propose a new approach to train a deep neural network with multiple intermediate auxiliary classifiers, branching from it. These 'multi-exits' ...

Apr 7, 2024 · Confidence-based early exiting methods utilize confi- dence, entropy, or (calibrated) max class probability to exit. In DeeBERT (Xin et al. 2020) ...

Missing: Probabilistic | Show results with:Probabilistic

Pomponi, J., Scardapane, S., and Uncini, A. (2022). A Probabilistic Re-Interpretation of Confidence Scores in Multi-Exit Models. Entropy, 24. https://doi.org/ ...

Missing: Exit | Show results with:Exit

Apr 28, 2024 · Confidence scores are essential as they provide an estimate of how likely it is that a model's prediction is correct. This is crucial for trust and safety in ...