Explaining Distributed Neural Activations via Unsupervised Learning

By Soheil Kolouri, Charles E. Martin, Heiko Hoffmann. 2017

8 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

1. Song-Chun Zhu
2. Quanshi Zhang
3. Ying Nian Wu
4. Mohammad Rostami
5. Yu Yang
6. Eric Eaton
7. Soheil Kolouri
8. Alan L. Yuille

Citations in the corpus

• Unsupervised Learning of Neural Networks to Explain Neural Networks (2018, background): “Other studies retrieved mid-level representations with specific meanings from CNNs for various applications [15, 35, 32, 19].”
• Interpretable Convolutional Neural Networks (2017, background): “[11, 31, 29, 15] selected neural units with specific meanings from CNNs for various applications.”
• Deep Transfer Learning for Few-Shot SAR Image Classification (2019, background): “…are task-dependent, this often leads to better performance compared to engineered features that are usually defined for a broad range of tasks, e.g., wavelet, DFT, SIFT, etc. Partial early results of this work will be presented at the 2019 CVPR workshop on Perception Beyond the Visible Spectrum [1].”