Topological Obstructions and How to Avoid Them
Authors: Babak Esmaeili, Robin Walters, Heiko Zimmermann, Jan-Willem van de Meent
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we theoretically and empirically characterize obstructions to training encoders with geometric latent spaces... We empirically evaluate our model on 2 domains. We observe improved stability during training and a higher chance of converging to a homeomorphic encoder. |
| Researcher Affiliation | Academia | Babak Esmaeili Generative AI Group Eindhoven University of Technology b.esmaeili@tue.nl Robin Walters Khoury College of Computer Sciences Northeastern University r.walters@northeastern.edu Heiko Zimmermann Amsterdam Machine Learning Lab University of Amsterdam h.zimmermann@uva.nl Jan-Willem van de Meent Amsterdam Machine Learning Lab University of Amsterdam j.w.vandemeent@uva.nl |
| Pseudocode | No | The paper includes architectural diagrams (Figure 1) but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it include a specific repository link or an explicit code release statement. |
| Open Datasets | Yes | We train on images of an L-shaped tetromino [Bozkurt et al., 2021], a teapot, and an airplane [Shilane et al., 2004]. The SO(2) manifold corresponding to each object is made by rotating the image of the object around the center. |
| Dataset Splits | No | The paper does not provide specific dataset split information, such as exact percentages, sample counts, or citations to predefined splits, for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details, such as exact GPU/CPU models, processor types, or memory amounts, used for running its experiments. |
| Software Dependencies | No | The paper mentions using the 'RAdam optimizer [Liu et al., 2019]' but does not provide specific version numbers for any software dependencies or libraries used to replicate the experiment. |
| Experiment Setup | Yes | We train all our models for 150 epochs with a batch-size of 600. For optimization, we use the RAdam optimizer [Liu et al., 2019] with a learning rate of 5e-4. All models were initialized and trained with 153 different random seeds. For all image datasets, we use a 4-layer CNN with kernel, stride, and padding of size 4, 2 and 1 respectively followed by a leaky Re LU activation (Table 2) for the encoder and a Sigmoid activation for the decoder. |