Invariant representation learning for robust deep networks

By Julian Salazar. 2018

4 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Representation & Self-Supervised Learning

1. Quoc V. Le
2. Minh-Thang Luong
3. Zihang Dai
4. Qizhe Xie
5. Eduard H. Hovy
6. Jian Zhang
7. Qiang Wang
8. Zhaobai Zhong

Citations in the corpus

• Unsupervised Data Augmentation for Consistency Training (2019, background): “UDA is also well-connected to invariant representation learning [37, 53] where the consistency loss is not only applied to the output layer but is also used for feature matching.”
• Adversarial AutoAugment (2019, methodology): “The target network is trained by a large batch formed by multiple augmented instances of each batch to promote invariant learning (Salazar et al., 2018), and the losses of different augmentation policies applied on the same data are used to train the augmentation policy network by RL algorithm.”