Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks
By Siddharth Roheda, Benjamin S. Riggan, Hamid Krim, Liyi Dai. 2018
37 citations recorded by the source corpus.
In this paper, we propose to use a Conditional Generative Adversarial Network (CGAN) for distilling (i.e. transferring) knowledge from sensor data and enhancing low-resolution target detection. In unconstrained surveillance settings, sensor measurements are often noisy, degraded, corrupted, and even missing/absent, thereby presenting a significant problem for multi-modal fusion. We therefore specifically tackle the problem of a missing modality in our attempt to propose an algorithm based on CGANs to generate representative information from the missing modalities when given some other available modalities. Despite modality gaps, we show that one can distill knowledge from one set of modalities to another. Moreover, we demonstrate that it achieves better performance than traditional approaches and recent teacher-student models.
Subfields: Computer Vision and Pattern Recognition, Ocean Engineering, Oceanography
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
- Dacheng Tao
- Jianping Gou
- S. Maybank
- Baosheng Yu
- Luc Van Gool
- Trevor Darrell
- Xiaogang Wang
- Ming-Hsuan Yang
In Ocean Engineering
Citations in the corpus
- Knowledge Distillation: A Survey (2020, background): “In essence, this distillation shares the cross-modal knowledge among the teachers and students (Perez et al. 2020; Albanie et al. 2018; Roheda et al. 2018).”