Simple Disentanglement of Style and Content in Visual Representations
Authors: Lilian Ngweta, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify its efficacy empirically. Our post-processed features yield significant domain generalization performance improvements... We verify the ability of PISCO to disentangle style and content on three image datasets of varying size and complexity via post-processing of various pre-trained deep visual feature extractors. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York, United States 2Department of Statistics, University of Michigan, Ann Arbor, Michigan, United States 3IBM Research, Cambridge, Massachusetts, United States 4MIT-IBM Watson AI Lab, Cambridge, Massachusetts, United States. |
| Pseudocode | Yes | Algorithm 1 PISCO |
| Open Source Code | Yes | The experiments code is available on Git Hub.2 Code: github.com/lilianngweta/PISCO. |
| Open Datasets | Yes | We consider nine transformations in our experiments: four types of image corruptions (rotation, contrast, blur, and saturation) on CIFAR-10 (Krizhevsky et al., 2009)... on Image Net (Russakovsky et al., 2015)... and a color transformation on MNIST, similar to Colored MNIST (Arjovsky et al., 2019)... |
| Dataset Splits | Yes | Specifically, in the training dataset, we corrupt images from the first half of the classes with probability α and from the second half of the classes with probability 1 α. In test data the correlation is reversed, i.e., images from the first half of the classes are corrupted with probability 1 α and images from the second half with probability α (see C for details). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Pytorch’s Torchvision package" but does not specify version numbers for this or any other key software dependencies. |
| Experiment Setup | Yes | The batch size used when training the logistic regression model on Image Net was 32768, the learning rate was 0.0001, and the number of epochs was 50. |