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Found 2 papers, 1 papers with code
Learning from Emergence: A Study on Proactively Inhibiting the Monosemantic Neurons of Artificial Neural Networks
We validate our conjecture that monosemanticity brings about performance change at different model scales on a variety of neural networks and benchmark datasets in different areas, including language, image, and physics simulation tasks.
Noise2Info: Noisy Image to Information of Noise for Self-Supervised Image Denoising
However, such a method is highly sensitive to the standard deviation \sigma_n of noises injected to clean images, where \sigma_n is inaccessible without knowing clean images.
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