MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural Networks Article Swipe
Claudio Filipi Goncalves Santos
,
Mateus Roder
,
Leandro A. Passos
,
João Paulo Papa
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.02740
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.02740
In the last decade, exponential data growth supplied the machine learning-based algorithms' capacity and enabled their usage in daily life activities. Additionally, such an improvement is partially explained due to the advent of deep learning techniques, i.e., stacks of simple architectures that end up in more complex models. Although both factors produce outstanding results, they also pose drawbacks regarding the learning process since training complex models denotes an expensive task and results are prone to overfit the training data. A supervised regularization technique called MaxDropout was recently proposed to tackle the latter, providing several improvements concerning traditional regularization approaches. In this paper, we present its improved version called MaxDropoutV2. Results considering two public datasets show that the model performs faster than the standard version and, in most cases, provides more accurate results.
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- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2203.02740
- https://arxiv.org/pdf/2203.02740
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221154392
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https://openalex.org/W4221154392Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2203.02740Digital Object Identifier
- Title
-
MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural NetworksWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-03-05Full publication date if available
- Authors
-
Claudio Filipi Goncalves Santos, Mateus Roder, Leandro A. Passos, João Paulo PapaList of authors in order
- Landing page
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https://arxiv.org/abs/2203.02740Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2203.02740Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2203.02740Direct OA link when available
- Concepts
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Overfitting, Computer science, Regularization (linguistics), Artificial intelligence, Machine learning, Convolutional neural network, Deep learning, Deep neural networks, Task (project management), Process (computing), Training set, Artificial neural network, Engineering, Operating system, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Responsible consumption and production |
| citation_normalized_percentile |