Summary of Comparison Of Embedded Spaces For Deep Learning Classification, by Stefan Scholl
Comparison of Embedded Spaces for Deep Learning Classification
by Stefan Scholl
First submitted to arxiv on: 3 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive overview of various techniques for designing embedded spaces in deep learning, focusing on classification tasks. By comparing different loss functions and constraints on network parameters, the study demonstrates how to achieve desired geometric structures in these spaces. The proposed methods are evaluated using 2D and 3D embeddings on MNIST, Fashion MNIST, and CIFAR-10 datasets, enabling visual inspection of the resulting embedded spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding better ways to represent data in deep learning models so they can be used for tasks like classifying images or understanding why a model makes certain predictions. The researchers show different methods for creating these “embedded spaces” and how well each method works. They use popular datasets like MNIST, Fashion MNIST, and CIFAR-10 to test their ideas. |
Keywords
» Artificial intelligence » Classification » Deep learning
