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

Abstract: Generative models, including diffusion models, are increasingly used as foundation models and adapted through sequential fine-tuning, making continual learning an essential problem setting. However, continual learning in such generative models remains poorly understood: after a task change, what aspects of the learned distribution are most easily lost, and what replay samples should be prioritized? We address these questions through the modern Hopfield energy. Recent links between modern Hopfield networks (MHNs) and diffusion models allow analyses in MHNs to be transferred to diffusion models. We introduce intrinsic forgetting as an increase in Hopfield energy after the task change. In tractable settings in an MHN, we prove that high-energy, outlier-like samples undergo a larger energy increase than cluster-like samples, implying that samples located in sharp, isolated basins are more forgettable. We further analyze memory replay and show that replay is particularly effective for high-energy samples, enabling an energy-based selection of replay samples. We validate these predictions in experiments on MHNs and two diffusion models under continual-learning settings: Stable Diffusion and a pixel-space DDPM. In these diffusion models, Hopfield energy tracks reconstruction-based forgetting, and replay experiments reveal energy-dependent mitigation of forgetting that is consistent with the MHN analysis.

• The paper introduces a novel framework leveraging Hopfield energy as an intrinsic measure to quantify forgetting in continual learning settings.
• It demonstrates that high-energy samples in diffusion models experience exponentially larger forgetting, validated on Stable Diffusion and DDPM.
• The study proposes an energy-dependent replay strategy that prioritizes high-energy samples to effectively mitigate catastrophic forgetting.

Continual Learning in Modern Hopfield Networks and Diffusion Models: Intrinsic Forgetting and Replay

Hopfield Energy as a Measure of Forgetting in Generative Models

The paper "Continual Learning in Modern Hopfield Networks with an Application to Diffusion Models" (2605.27975) provides a theoretical and empirical framework for analyzing continual learning (CL) in unsupervised generative models, specifically diffusion models, through the lens of modern Hopfield networks (MHNs). The central thesis is that the Hopfield energy function is a robust intrinsic indicator for quantifying forgetting during sequential adaptation. The connection between MHNs and diffusion models allows precise mathematical analysis and practical algorithms to be transferred between these domains.

The Hopfield energy, $E(\xi\mid X)$, naturally carves the state space into broad, low-energy basins for typical samples and sharp, high-energy basins for outliers or rare states. An increase in Hopfield energy after a task change is termed intrinsic forgetting: the generative model assigns a reduced probability to the forgotten state. Analytically, this energy increase is much more pronounced for outlier-like (high-energy) memories compared to cluster-supported (low-energy) memories. This effect is formalized and proven within tractable MHN settings, and is illustrated in (Figure 1).

Figure 1: The Hopfield energy structure creates wide, low-energy basins around typical samples and sharp, high-energy basins around outliers; task transitions shift the landscape, increasing energy sharply for outlier-side memories.

Theoretical Results: Intrinsic Forgetting and Replay in MHNs

The analysis uses a synthetic scenario with equal-angular clusters and orthogonal outliers. For a rotation-induced task change, the energy increase at a memory $\xi^*$ is calculated as:

$$\Delta E^* = E(\xi^* \mid X_{\text{new}}) - E(\xi^* \mid X_{\text{old}})$$

For high-energy (isolated) samples, this increase is exponentially larger than for cluster-centric memories, with leading-order control provided by off-memory softmax weights. The localization regime ($\beta\gg\log N$) ensures robust pattern-specific fixed points and clean separation between cluster and outlier dynamics.

Theorem 1 establishes that $$\Delta E_{\text{outlier}} \gg \Delta E_{\text{cluster}}$$: memories in high-energy basins are far more susceptible to forgetting during task transitions. This is verified experimentally with MHN-BM and closed-form MHN simulations (Figure 2).

Figure 2: MHN-BM shows larger intrinsic forgetting (energy rise) for outlier memories compared to cluster memories under synthetic rotations.

Empirical Validation in Diffusion Models

These theoretical predictions are transferred to diffusion models via the established correspondence between score-based generative diffusion and Hopfield attractor dynamics. The paper operationalizes Hopfield energy per image for both Stable Diffusion and pixel-space DDPM on split CIFAR-10. Reconstruction-based forgetting is measured via post-task reverse trajectories: the mean squared error (MSE) between the original image and its reconstruction after Task 2 training.

Across both diffusion models, there exists a strict monotonic relationship: higher Hopfield energy correlates with larger reconstruction-based forgetting (Figure 3, Figure 4). This finding is robust to architectural variants and dataset domain shifts.

Figure 3: Forgetting is strictly monotonic with Hopfield energy in Stable Diffusion v1.5 on split CIFAR-10.

Figure 4: Pixel-space DDPM yields similar monotonic increase of reconstruction error with Hopfield energy.

Energy-Dependent Replay and Sample Selection

The second major contribution concerns replay prioritization. Theoretical analysis proves that replay of high-energy samples achieves the most substantial reduction in intrinsic forgetting, while within-cluster cross-replay or cross-type replay provide exponentially weaker gains.

Theorem 2 formalizes that self-replay of high-energy outliers yields the largest energy rescue due to the product of replay susceptibility ($\rho_{r\mid k}$) and energy rise ($\Delta E_k^{\text{fp}}$). The ordering of replay effectiveness leads to a concrete policy: construct a replay buffer by selecting the top-$K$ highest-energy samples from Task 1.

Empirical results with Stable Diffusion and pixel-space DDPM corroborate this theoretical hierarchy (Figure 5, Figure 6, Figure 7, Figure 8). High-energy replay generates strong but localized improvements in forgetting mitigation, while low-energy replay yields weaker, but more broadly distributed effects across the memory landscape.

Figure 5: Energy vs. perturbation sharpness; higher energy samples are sharper and more susceptible to forgetting.

Figure 6: Closed-form MHN corroboration of Theorem 1: outlier fixed points exhibit larger energy rise.

Figure 7: Closed-form MHN corroboration of the replay ordering in Theorem 2.

Figure 8: High-energy replay in diffusion models increases the rescue effect for high-energy samples, in agreement with theoretical predictions.

Implications and Future Directions

The results robustly demonstrate that Hopfield energy is an intrinsic, quantitative measure of knowledge retention and loss in generative models under continual learning. The practical implication is clear: energy-based replay buffers can be constructed without reliance on external classifiers or ad hoc task labels, enabling principled mitigation strategies for catastrophic forgetting in real-world generative adaptation.

On a theoretical level, energy geometry, basin sharpness, and replay susceptibility define the sample-level dynamics of memory in unsupervised architectures. These findings bridge associative memory theory and modern generative modeling, suggesting that energy landscape diagnostics can serve as foundational tools for investigating generalization, forgetting, and capacity in diffusion architectures.

Future developments may include:

• Extension to non-image modalities and distributional shifts beyond rotations or class splits.
• Application to online continual learning without explicit task boundaries.
• Investigation of finite-sample and data-manifold regimes, where memory capacity scales with input dimensionality.
• Integration of more detailed energy-based indicators (e.g., probability-flow ODE marginals) for tighter correlations in forgetting diagnostics.

Conclusion

This paper rigorously establishes the Hopfield energy as a reliable intrinsic indicator of forgetting and as an actionable criterion for replay prioritization in both MHNs and diffusion models undergoing continual learning. The analysis is supported by strong theoretical proofs and empirical validations across synthetic and practical generative systems. The geometry of the learned energy landscape, specifically basin sharpness and energy level, governs the selective vulnerability and mitigation of memories during sequential adaptation. The framework offers a unifying, principled approach for diagnostic and intervention strategies in unsupervised continual learning settings.