Deep Generative Continual Learning using Functional LoRA: FunLoRA

• URL: http://arxiv.org/abs/2510.02631v1
• Date: Fri, 03 Oct 2025 00:18:05 GMT
• Title: Deep Generative Continual Learning using Functional LoRA: FunLoRA
• Authors: Victor Enescu, Hichem Sahbi,
• Abstract summary: A common strategy consists in retraining the generative model on its own synthetic data in order to mitigate forgetting.<n>We propose a novel and more expressive conditioning mechanism for generative models based on low rank adaptation (LoRA)<n>Our proposed parameter-efficient fine-tuning (PEFT) method surpasses prior state-of-the-art results based on diffusion models.
• Score: 12.547444644243543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continual adaptation of deep generative models holds tremendous potential and critical importance, given their rapid and expanding usage in text and vision based applications. Incremental training, however, remains highly challenging due to catastrophic forgetting phenomenon, which makes it difficult for neural networks to effectively incorporate new knowledge. A common strategy consists in retraining the generative model on its own synthetic data in order to mitigate forgetting. Yet, such an approach faces two major limitations: (i) the continually increasing training time eventually becomes intractable, and (ii) reliance on synthetic data inevitably leads to long-term performance degradation, since synthetic samples lack the richness of real training data. In this paper, we attenuate these issues by designing a novel and more expressive conditioning mechanism for generative models based on low rank adaptation (LoRA), that exclusively employs rank 1 matrices, whose reparametrized matrix rank is functionally increased using carefully selected functions -- and dubbed functional LoRA: FunLoRA. Using this dynamic conditioning, the generative model is guaranteed to avoid catastrophic forgetting and needs only to be trained on data from the current task. Extensive experiments using flow-matching based models trained from scratch, showcase that our proposed parameter-efficient fine-tuning (PEFT) method surpasses prior state-of-the-art results based on diffusion models, reaching higher classification accuracy scores, while only requiring a fraction of the memory cost and sampling time.

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