Topologically Densified Distributions
Authors: Christoph Hofer, Florian Graf, Marc Niethammer, Roland Kwitt
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we provide empirical evidence (across various vision benchmarks) to support our claim for better generalization. For our experiments, we draw on a setup common to many works in semi-supervised learning (...) In particular, we present experiments on three (10 class) vision benchmark datasets: MNIST, SVHN and CIFAR10. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Univ. of Salzburg, Austria 2Univ. of North Carolina, Chapel Hill, USA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Py Torch source code is available at https://github.com/c-hofer/topologically_densified_distributions |
| Open Datasets | Yes | In particular, we present experiments on three (10 class) vision benchmark datasets: MNIST, SVHN and CIFAR10. |
| Dataset Splits | Yes | Thus, we study the behavior of crossvalidating β, when the validation set is of size equal to the training corpus. The shaded region shows the variation in the testing error on small-validation sets. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU, CPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch source code' but does not specify a version number or list other software dependencies with their versions. |
| Experiment Setup | Yes | Optimization is done by SGD with momentum (0.9) over 310 epochs with cross-entropy loss and cosine learning rate annealing (Loshchilov & Hutter, 2017) (without restarts). As all experiments use weight decay, it is important to note that batch normalization combined with weight decay mainly affects the effective learning rate (van Laarhoven, 2017; Zhang et al., 2019). (...) We choose a sub-batch size of b = 16 and draw n = 8 sub-batches (see 2.4); this amounts to a total batch size of 128. |