Learning Hierarchical Priors in VAEs
Authors: Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our approach, we consider the following experiments. In Sec. 4.1, we demonstrate that our method learns to represent the degree of freedom in the data of a moving pendulum. In Sec. 4.2, we generate human movements based on the learned latent representations of real-world data (CMU Graphics Lab Motion Capture Database). In Sec. 4.3, the marginal log-likelihood on standard datasets such as MNIST, Fashion-MNIST, and OMNIGLOT is evaluated. In Sec. 4.4, we compare our method on the high-dimensional image datasets 3D Faces and 3D Chairs. |
| Researcher Affiliation | Collaboration | 1Machine Learning Research Lab, Volkswagen Group, Germany 2Department of Informatics, Technical University of Munich, Germany |
| Pseudocode | Yes | Algorithm 1 (REWO) Reconstruction-error-based weighting of the objective function |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | CMU Graphics Lab Motion Capture Database (http://mocap.cs.cmu.edu), MNIST [18, 17], Fashion-MNIST [29], and OMNIGLOT [16] |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python, specific deep learning frameworks like TensorFlow or PyTorch, with their versions). |
| Experiment Setup | Yes | In the GECO update scheme (Eq. (5)), β increases/decreases until ˆCt = κ2. ... βt = βt 1 exp ν fβ(βt 1, ˆCt κ2; τ) (ˆCt κ2) ... Initialise β 1 ... ˆCt = (1 α) ˆCba + α ˆCt 1 |