Metadata-Version: 2.1
Name: multiviewdata
Version: 0.1.4
Summary: Packaged data modules for multiview learning benchmarks
Home-page: UNKNOWN
Author: James Chapman
Author-email: james.chapman.19@ucl.ac.uk
License: UNKNOWN
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        # Multiview Data
        
        * Experimental package to give easy access to key toy and simulated datasets from the (deep) multiview learning literature
        * Feedback and contributions are welcome
        
        ## Getting Started
        
        Datasets are imported and built with the following syntax:
        
        ```python
        import os
        from multiviewdata.torchdatasets import XRMBDataset
        
        my_dataset = XRMBDataset(root=os.getcwd(),download=True)
        ```
        
        Datasets have somewhat standardised batches. 
        
        ```python
        my_dataset[0]['index'] # returns the index of the batch element
        my_dataset[0]['views'] # returns a tuple/list of each view
        ```
        
        Individual datasets may have additional information such as "label", "partial", and "userid".
        For more information check the docs for each dataset.
        
        ## Roadmap
        
        * option to convert torch datasets to dictionaries of numpy arrays to allow for batch methods
        * additional datasets
        * standardised plotting functions for each dataset?
        * benchmarks?
        
        ## Sources
        
        ### XRMB
        https://home.ttic.edu/~klivescu/XRMB_data/full/README
        
        This directory contains data based on the University of Wisconsin X-ray Microbeam Database (referred to here as XRMB).
        
        The original XRMB manual can be found here:  http://www.haskins.yale.edu/staff/gafos_downloads/ubdbman.pdf
        
        We acknowledge John Westbury for providing the original data and for permitting this post-processed version to be redistributed.  The original data collection was supported (in part) by research grant number R01 DC 00820 from the National Institute of Deafness and Other Communicative Disorders, U.S. National Institutes of Health.
        
        The post-processed data provided here was produced as part of work supported in part by NSF grant IIS-1321015.
        
        Some of the original XRMB articulatory data was missing due to issues such as pellet tracking errors.  The data has been reconstructed in using the algorithm described in this paper:  
        
        Wang, Arora, and Livescu, "Reconstruction of articulatory measurements with smoothed low-rank matrix completion," SLT 2014.
        http://ttic.edu/livescu/papers/wang_SLT2014.pdf
        
        The data provided here has been used for multi-view acoustic feature learning in this paper:
        
        Wang, Arora, Livescu, and Bilmes, "Unsupervised learning of acoustic features via deep canonical correlation analysis," ICASSP 2015.
        http://ttic.edu/livescu/papers/wang_ICASSP2015.pdf
        
        If you use this version of the data, please cite the papers above.
        
        ### WIW
        https://github.com/rotmanguy/DPCCA
        MIT License
        
        ### Cars3d
        https://github.com/llvqi/multiview_and_self-supervision
        Apache License 2.0
        
        ### MNIST
        https://github.com/bcdutton/AdversarialCanonicalCorrelationAnalysis
        Unlicensed
        
        ### MFeat
        
        
        ### Twitter
        https://github.com/abenton/wgcca
        MIT License
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