Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Intrinsic dimensionality estimation using Normalizing Flows
Authors: Christian Horvat, Jean-Pascal Pfister
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method on various datasets, including 64x64 RGB images, where we achieve state-of-the-art results. We benchmark our method with two NN and LIDL, see Section 3. The latter is very similar to our method as we both inflate the manifold with different values of σ2 and then use an NF to learn qσ2. |
| Researcher Affiliation | Academia | Christian Horvat Department of Physiology University of Bern EMAIL Jean-Pascal Pfister Department of Physiology Bern, Switzerland EMAIL |
| Pseudocode | Yes | Algorithm 4: Estimating the intrinsic dimensionality given a set of data points. |
| Open Source Code | Yes | The code for using ID-NF or reproducing our experiments can be found here https://github.com/chrvt/ID-NF. |
| Open Datasets | Yes | We test our method on various synthetic datasets with known ID: a sphere, torus, hyperboloid, thin spiral, swiss roll, and Stiefel manifold, see Table 1. We apply our method on the Style Gan d = 2 and d = 64 image manifold...FFHQ dataset [18]. Celeb A dataset [24]. |
| Dataset Splits | No | The paper mentions using training sets and implicitly testing, but does not specify clear train/validation/test splits with percentages, counts, or explicit standard split references. |
| Hardware Specification | No | Did you include the total amount of comute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No] |
| Software Dependencies | No | The paper mentions training details are in the supplemental material, but the main text does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | No | We refer to the supplementary for corresponding training details and additional figures. |