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Split computing and early exiting for deep learning applications: Survey and research challenges
Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep
neural networks (DNNs) to execute complex inference tasks such as image classification …
neural networks (DNNs) to execute complex inference tasks such as image classification …
Lgvit: Dynamic early exiting for accelerating vision transformer
Recently, the efficient deployment and acceleration of powerful vision transformers (ViTs) on
resource-limited edge devices for providing multimedia services have become attractive …
resource-limited edge devices for providing multimedia services have become attractive …
[HTML][HTML] Single-layer vision transformers for more accurate early exits with less overhead
Deploying deep learning models in time-critical applications with limited computational
resources, for instance in edge computing systems and IoT networks, is a challenging task …
resources, for instance in edge computing systems and IoT networks, is a challenging task …
Towards anytime classification in early-exit architectures by enforcing conditional monotonicity
Modern predictive models are often deployed to environments in which computational
budgets are dynamic. Anytime algorithms are well-suited to such environments as, at any …
budgets are dynamic. Anytime algorithms are well-suited to such environments as, at any …
[HTML][HTML] Zero time waste in pre-trained early exit neural networks
The problem of reducing processing time of large deep learning models is a fundamental
challenge in many real-world applications. Early exit methods strive towards this goal by …
challenge in many real-world applications. Early exit methods strive towards this goal by …
Fiancee: Faster inference of adversarial networks via conditional early exits
Generative DNNs are a powerful tool for image synthesis, but they are limited by their
computational load. On the other hand, given a trained model and a task, eg faces …
computational load. On the other hand, given a trained model and a task, eg faces …
Efficiently controlling multiple risks with pareto testing
Machine learning applications frequently come with multiple diverse objectives and
constraints that can change over time. Accordingly, trained models can be tuned with sets of …
constraints that can change over time. Accordingly, trained models can be tuned with sets of …
Occamnets: Mitigating dataset bias by favoring simpler hypotheses
Dataset bias and spurious correlations can significantly impair generalization in deep neural
networks. Many prior efforts have addressed this problem using either alternative loss …
networks. Many prior efforts have addressed this problem using either alternative loss …
Meta-GF: Training dynamic-depth neural networks harmoniously
Y Sun, J Li, X Xu - European Conference on Computer Vision, 2022 - Springer
Most state-of-the-art deep neural networks use static inference graphs, which makes it
impossible for such networks to dynamically adjust the depth or width of the network …
impossible for such networks to dynamically adjust the depth or width of the network …
Scrollnet: Dynamicweight importance for continual learning
The principle underlying most existing continual learning (CL) methods is to prioritize
stability by penalizing changes in parameters crucial to old tasks, while allowing for plasticity …
stability by penalizing changes in parameters crucial to old tasks, while allowing for plasticity …