Summary of Towards Utilising a Range Of Neural Activations For Comprehending Representational Associations, by Laura O’mahony et al.
Towards Utilising a Range of Neural Activations for Comprehending Representational Associations
by Laura O’Mahony, Nikola S. Nikolov, David JP O’Sullivan
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Recent research has focused on understanding intermediate representations in deep neural networks by examining individual neurons and their combinations that form linear directions in the latent space. However, this approach may not capture valuable information about representation behavior. In reality, neural network activations are typically dense, making it a more complex scenario where linear directions encode information at various levels of stimulation. This paper hypothesizes that non-extremal level activations contain valuable information, such as statistical associations, worth investigating to locate confounding human interpretable concepts. The study explores the value of studying range neuron activations by analyzing mid-level output neuron activations on a synthetic dataset, demonstrating how they can inform about representation aspects in the penultimate layer not evident through maximal activation analysis alone. A method is developed to curate data from mid-range logit samples for retraining to mitigate spurious correlations or confounding concepts in the penultimate layer on real benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how deep neural networks work and tries to understand what’s going on inside them. Some people think that by looking at special “extreme” points, they can figure out what these networks are learning. But this paper says that’s not the whole story. Neural networks are usually very busy and many parts of them are working together. This paper thinks that we should look at all the different levels of activity in the network to understand how it’s really working. They tested this idea on a fake dataset and showed that by looking at the middle-level “output” neurons, they could learn new things about how the network works. |
Keywords
» Artificial intelligence » Latent space » Neural network
