Continual Learning in Modern Hopfield Networks with an Application to Diffusion Models

2026-05-27 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, medium

Summary

A study investigates continual learning in generative models, including diffusion models, by analyzing forgetting mechanisms and replay strategies using modern Hopfield networks (MHNs). The research introduces "intrinsic forgetting" as an increase in Hopfield energy after a task change. It proves that high-energy, outlier-like samples experience a greater energy increase and are thus more forgettable than cluster-like samples, which reside in sharp, isolated basins. Furthermore, the analysis demonstrates that memory replay is especially effective for these high-energy samples, suggesting an energy-based method for selecting replay data. These theoretical predictions are validated through experiments on MHNs, Stable Diffusion, and a pixel-space DDPM, where Hopfield energy accurately tracks reconstruction-based forgetting and shows consistent energy-dependent forgetting mitigation.

Key takeaway

For AI Scientists managing continual learning in generative models like Stable Diffusion or DDPMs, understanding forgetting patterns is crucial. You should prioritize memory replay for high-energy, outlier-like samples, as these are proven to be most susceptible to catastrophic forgetting. Utilizing Hopfield energy as a metric allows for targeted replay sample selection, significantly mitigating performance degradation on old tasks and improving the stability-plasticity trade-off in sequential fine-tuning.

Key insights

High-energy, outlier-like samples are more prone to forgetting in continual learning for generative models, and replay is most effective for them.

Principles

• Forgetting in generative models correlates with increased Hopfield energy.
• Samples in sharp, isolated basins are more forgettable.
• Energy-based selection optimizes memory replay effectiveness.

Method

The paper introduces intrinsic forgetting as an increase in Hopfield energy after a task change, then analyzes its impact on different sample types and the effectiveness of memory replay based on this energy.

In practice

• Prioritize high-energy, outlier-like samples for memory replay.
• Use Hopfield energy to identify forgettable samples in diffusion models.

Topics

• Continual Learning
• Hopfield Networks
• Diffusion Models
• Generative Models
• Catastrophic Forgetting
• Memory Replay

Code references

• ml-jku/hopfield-layers
• ChenWu98/cycle-diffusion

Best for: Research Scientist, Computer Vision Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.