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Found 6 papers, 2 papers with code
Fast OT for Latent Domain Adaptation
This is achieved by minimizing the cost of transporting the samples from the target domain to the distribution of the source domain.
Latent Code-Based Fusion: A Volterra Neural Network Approach
We propose a deep structure encoder using the recently introduced Volterra Neural Networks (VNNs) to seek a latent representation of multi-modal data whose features are jointly captured by a union of subspaces.
Conquering the cnn over-parameterization dilemma: A volterra filtering approach for action recognition
The importance of inference in Machine Learning (ML) has led to an explosive number of different proposals in ML, and particularly in Deep Learning.
Volterra Neural Networks (VNNs)
The importance of inference in Machine Learning (ML) has led to an explosive number of different proposals in ML, and particularly in Deep Learning.
Robust Multi-Modal Sensor Fusion: An Adversarial Approach
Exploiting complementary information from different sensors, we show that target detection and classification problems can greatly benefit from this fusion approach and result in a performance increase.
Cross-Modality Distillation: A case for Conditional Generative Adversarial Networks
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.
