Stochastic Latent Residual Video Prediction
Authors: Jean-Yves Franceschi, Edouard Delasalles, Mickael Chen, Sylvain Lamprier, Patrick Gallinari
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section exposes the experimental results of our method on four standard stochastic video prediction datasets.1 We compare our method with state-of-the-art baselines on stochastic video prediction. Furthermore, we qualitatively study the dynamics and latent space learned by our model.2 |
| Researcher Affiliation | Collaboration | 1Sorbonne Université, CNRS, LIP6, F75005 Paris, France 2Criteo AI Lab, Paris, France. |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | Code and datasets are available at https://github.com/edouardelasalles/srvp. |
| Open Datasets | Yes | We present experimental results on a simulated dataset and three real-world datasets, that we briefly present in the following and detail in Appendix B. The corresponding numerical results can be found in Appendix D. For the sake of concision, we only display a handful of qualitative samples in this section, and refer to Appendix H and our website for additional samples. We compare our model against several variational state-of-the-art models: SV2P (Babaeizadeh et al., 2018), SVG (Denton & Fergus, 2018), SAVP (Lee et al., 2018), and Struct VRNN (Minderer et al., 2019). |
| Dataset Splits | No | The paper mentions 'Training details are described in Appendix C' and refers to 'test sequence' and 'training sequences', but does not provide specific percentages, sample counts, or explicit methodology for training/validation/test splits in the main text. |
| Hardware Specification | No | This work was granted access to the HPC resources of IDRIS under the allocation 2020-AD011011360 made by GENCI (Grand Equipement National de Calcul Intensif). |
| Software Dependencies | No | The paper mentions 'PyTorch' in the references but does not provide specific version numbers for it or other software dependencies. |
| Experiment Setup | No | Training details are described in Appendix C. |