  The success of deep learning models has led to their adaptation and adoption
by prominent video understanding methods. The majority of these approaches
encode features in a joint space-time modality for which the inner workings and
learned representations are difficult to visually interpret. We propose LEArned
Preconscious Synthesis (LEAPS), an architecture-independent method for
synthesizing videos from the internal spatiotemporal representations of models.
Using a stimulus video and a target class, we prime a fixed space-time model
and iteratively optimize a video initialized with random noise. Additional
regularizers are used to improve the feature diversity of the synthesized
videos alongside the cross-frame temporal coherence of motions. We
quantitatively and qualitatively evaluate the applicability of LEAPS by
inverting a range of spatiotemporal convolutional and attention-based
architectures trained on Kinetics-400, which to the best of our knowledge has
not been previously accomplished.
