Pooling Image Datasets with Multiple Covariate Shift and Imbalance
Authors: Sotirios Panagiotis Chytas, Vishnu Suresh Lokhande, Vikas Singh
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the effectiveness of this approach via extensive experiments on real datasets. Further, we discuss how this style of formulation offers a unified perspective on at least 5+ distinct problem settings, from self-supervised learning to matching problems in 3D reconstruction. The code is available at https://github.com/SPChytas/CatHarm. and 5 EXPERIMENTAL EVALUATIONS |
| Researcher Affiliation | Academia | Sotirios Panagiotis Chytas UW-Madison Vishnu Suresh Lokhande UW-Madison Vikas Singh UW-Madison |
| Pseudocode | Yes | Algorithm 1 Structure preserving training of Functors |
| Open Source Code | Yes | The code is available at https://github.com/SPChytas/CatHarm. |
| Open Datasets | Yes | The ADNI dataset can be obtained from https://adni.loni.usc.edu/. and Besides the medical image datasets, we evaluate our performance in two tabular datasets; the German(Hofmann, 1994) and the Adult Becker & Kohavi (1996). |
| Dataset Splits | Yes | All the experiments are a result of 5-fold cross validation procedure. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'modified Res Net' and 'Fully-Connected Neural network' but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We model the Functor F using a modified Res Net (He et al., 2016), and the Functor C using a Fully-Connected Neural network. For our experiments, using linear mappings W Rn n for the Morphisms in the target Category S (i.e., latent space) was sufficient but this can be easily upgraded. All the experiments are a result of 5-fold cross validation procedure. and In this experiment, we set n = 128. and In this experiment we set n = 32. and moderate values of λ (i.e. 0.01) lead to a low MMD value |