Using recently popularized invertible neural network We predict future video frames from complex dynamic scenes. Our invertible linear embedding (ILE) demonstrates successful learning, prediction and latent state inference. In contrast to other approaches, ILE does not use any explicit reconstruction loss or simplistic pixel-space assumptions. Instead, it leverages invertibility to optimize the likelihood of image sequences exactly, albeit indirectly.Experiments and comparisons against state of the art methods over synthetic and natural image sequences demonstrate the robustness of our approach, and a discussion of future work explores the opportunities our method might provide to other fields in which the accurate analysis and forecasting of non-linear dynamic systems is essential.
College and Department
Physical and Mathematical Sciences; Computer Science
BYU ScholarsArchive Citation
Pottorff, Robert Thomas, "Video Prediction with Invertible Linear Embeddings" (2019). Theses and Dissertations. 7577.
system identification, invertible neural networks, Hammerstein-Wiener, video, extrapolation