Connectivity-Optimized Representation Learning via Persistent Homology
Authors: Christoph Hofer, Roland Kwitt, Marc Niethammer, Mandar Dixit
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. One-class learning experiments ( 5) on large-scale vision data, showing that kernel-density based one-class models can be built on top of representations learned by a single autoencoder. These representations are transferable across datasets and, in a low sample size regime, our one-class models outperform recent stateof-the-art methods by a large margin. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of Salzburg, Austria 2Microsoft 3UNC Chapel Hill. |
| Pseudocode | No | The paper includes mathematical definitions and theorems but no structured pseudocode or algorithm blocks with typical formatting or explicit labels like 'Algorithm' or 'Pseudocode'. |
| Open Source Code | Yes | https://github.com/c-hofer/COREL_icml2019 |
| Open Datasets | Yes | CIFAR-10 (Krizhevsky & Hinton, 2009)... CIFAR-100... Tiny-Image Net... Image Net. For large-scale testing, we use the ILSVRC 2012 dataset (Deng et al., 2009) |
| Dataset Splits | Yes | CIFAR-10 (Krizhevsky & Hinton, 2009) contains 60,000 natural images of size 32 32 in 10 classes. 5,000 images/class are available for training, 1,000/class for validation. |
| Hardware Specification | Yes | On one GPU (Nvidia GTX 1080 Ti) this requires 75 hrs. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2017)' but does not provide a specific version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | The MLP is trained for 60 epochs with batch size 50 and η = 2. For optimization, we use Adam (Kingma & Ba, 2014) with a fixed learning rate of 0.001, (β1, β2) = (0.9, 0.999) and a batch-size of 100. The model is trained for 50 epochs. We fix η = 2 throughout our experiments. |