Interactive Image Classification on CIFAR-10

• Exploratory Analysis of CIFAR-10 Dataset
• Custom Metric Implementations for UMAP Dim Reduction on Image Datasets
• Custom Model Implementation of UMAP_MNN Dim Reduction

      A method for greater cluster separation utilizing UMAP with a mutual nearest neighbor graph
      Described in the paper:
      @article{Dalmia2021UMAPConnectivity,
        author={Ayush Dalmia and Suzanna Sia},
        title={Clustering with {UMAP:} Why and How Connectivity Matters},
        journal={CoRR},
        volume={abs/2108.05525},
        year={2021},
        url={https://arxiv.org/abs/2108.05525},
        eprinttype={arXiv},
        eprint={2108.05525},
        timestamp={Wed, 18 Aug 2021 19:45:42 +0200},
        biburl={https://dblp.org/rec/journals/corr/abs-2108-05525.bib},
        bibsource={dblp computer science bibliography, https://dblp.org}
        }
      and based on the implementation provided by the UMAP team in their documentation:
      "Improving the Separation Between Similar Classes Using a Mutual k-NN Graph"
      URL: https://umap-learn.readthedocs.io/en/latest/mutual_nn_umap.html
      and the method github following the path nearest neighbors notebook:
      URL: https://github.com/adalmia96/umap-mnn

• Custom Model Implementation of Unsupervised Deep Embedding for Clustering Analysis

      An autoencoder clustering method from the paper
      Unsupervised Deep Embedding for Clustering Analysis
      by Junyuan Xie, Ross Girshick, and Ali Farhadi
      https://arxiv.org/pdf/1511.06335.pdf
      and based on David Ko's example implementation of their method:
      https://ai-mrkogao.github.io/reinforcement%20learning/clusteringkeras/

• Brief Model Performance Base Case Comparisons
• Image Augmentation Techniques
• Interactive Web-Application using Gradio for Users to Try the Different Clustering Techniques + Dataset Augmentations