PatchUp: A Regularization Technique for Convolutional Neural Networks
By Mojtaba Faramarzi, Mohammad Amini, Akilesh Badrinaaraayanan, Vikas Verma, Sarath Chandar. 2020
39 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Quoc V. Le
- Minh-Thang Luong
- Luc Van Gool
- Dacheng Tao
- Mohit Bansal
- Trevor Darrell
- Alan L. Yuille
- Xiaogang Wang
Citations in the corpus
- A Survey of Data Augmentation Approaches for NLP (2021, background): “ing work has explored interpolating inner components (Verma et al., 2019; Faramarzi et al., 2020), more general mixing schemes (Guo, 2020), and adding adversaries (Beckham et al.”
- How Does Mixup Help With Robustness and Generalization? (2020, methodology): “This has motivated a recent line of work for developing a number of variants of Mixup, including Manifold Mixup (Verma et al., 2019a), Puzzle Mix (Kim et al., 2020), CutMix (Yun et al., 2019), Adversarial Mixup Resynthesis (Beckham et al., 2019), and PatchUp (Faramarzi et al., 2020).”
- Towards Domain-Agnostic Contrastive Learning (2020, background): “Mixup(Zhang et al., 2018; Tokozume et al., 2017) and its numerous variants(Verma et al., 2019a; Yun et al., 2019; Faramarzi et al., 2020) have seen remarkable success in supervised learning problems, as well other problems such as semi-supervised learning (Verma et al., 2019b; Berthelot et al.,…”
- Multi-modal Deep Learning (2024, methodology): “There are two primary methods for hidden layer mixup: Manifold Mixup [29] and Patch Up [22].”