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].
Phase Collapse in Neural Networks
Authors: Florentin Guth, John Zarka, Stéphane Mallat
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Table 1: Error of linear classifiers applied to a scattering (Scat), learned scattering (LScat) and learned scattering with skip connections (+ skip), on CIFAR-10 and Image Net. The last column gives the single-crop error of Res Net-20 for CIFAR-10 and Res Net-18 for Image Net, taken from https://pytorch.org/vision/stable/models.html. |
| Researcher Affiliation | Collaboration | Florentin Guth, John Zarka DI, ENS, CNRS, PSL University, Paris, France EMAIL Stéphane Mallat Collège de France, Paris, France Flatiron Institute, New York, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code to reproduce the experiments of the paper is available at https://github.com/ Florentin Guth/Phase Collapse. |
| Open Datasets | Yes | This section introduces a learned scattering transform, which is a highly structured CNN architecture relying on phase collapses and reaching Res Net accuracy on the Image Net (Russakovsky et al., 2015) and CIFAR-10 (Krizhevsky, 2009) datasets. |
| Dataset Splits | Yes | Classification error on Image Net validation set is computed on a single center crop of size 224. |
| Hardware Specification | Yes | All experiments ran during the preparation of this paper, including preliminary ones, required around 10k 32GB NVIDIA V100 GPU-hours. |
| Software Dependencies | No | The paper mentions using the 'Kymatio package' and 'SGD' optimizer but does not provide specific version numbers for these or other key software components. |
| Experiment Setup | Yes | We use the optimizer SGD with an initial learning rate of 0.01, a momentum of 0.9, a weight decay of 0.0001, and a batch size of 128. |