“Novelty Nets: Classifier Anti-Guidance”, 2024-02-23 (; backlinks):
Generative modeling proposal for increasing diversity of samples by a helper NN memorizing past samples and ‘repelling’ new samples away from old ones.
How can we avoid generative models always creating ‘same-y’ samples, particularly when prompts don’t work well? Novelty search approaches typically operate ‘outside’ the generative model, and so are hamstrung by the inherent non-novelty of the generative model’s usual sampling.
I propose novelty nets: small neural net adapter layers which are trained online during sampling to memorize the history of all previous samples, producing a ‘probability this is not novel’, and thus enable gradient descent to minimize that probability and yield a meaningfully-different new sample each time. This systematically increases the diversity and improves exploration & variation, as one no longer struggles to fight a model stubbornly insisting on generating extremely similar samples because that is just what it considers highly-likely or high-quality.
Novelty nets could be particularly useful for image generation, both at the user & service-level, as the nets push all samples collectively away from each other, reducing the esthetically unpleasant ‘same-y-ness’ of AI-generated images.