Title | ||
---|---|---|
Classification of sparsely labeled spatio-temporal data through semi-supervised adversarial learning. |
Abstract | ||
---|---|---|
In recent years, Generative Adversarial Networks (GAN) have emerged as a powerful method for learning the mapping from noisy latent spaces to realistic data samples in high-dimensional space. So far, the development and application of GANs have been predominantly focused on spatial data such as images. In this project, we aim at modeling of spatio-temporal sensor data instead, i.e. dynamic data over time. The main goal is to encode temporal data into a global and low-dimensional latent vector that captures the dynamics of the spatio-temporal signal. To this end, we incorporate auto-regressive RNNs, Wasserstein GAN loss, spectral norm weight constraints and a semi-supervised learning scheme into InfoGAN, a method for retrieval of meaningful latents in adversarial learning. To demonstrate the modeling capability of our method, we encode full-body skeletal human motion from a large dataset representing 60 classes of daily activities, recorded in a multi-Kinect setup. Initial results indicate competitive classification performance of the learned latent representations, compared to direct CNN/RNN inference. In future work, we plan to apply this method on a related problem in the medical domain, i.e. on recovery of meaningful latents in gait analysis of patients with vertigo and balance disorders. |
Year | Venue | Field |
---|---|---|
2018 | arXiv: Machine Learning | Spatial analysis,ENCODE,Inference,Matrix norm,Dynamic data,Temporal database,Artificial intelligence,Generative grammar,Mathematics,Machine learning,Adversarial system |
DocType | Volume | Citations |
Journal | abs/1801.08712 | 0 |
PageRank | References | Authors |
0.34 | 27 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Atanas Mirchev | 1 | 63 | 2.65 |
Seyed-Ahmad Ahmadi | 2 | 0 | 0.34 |