Newest papers

Trajectory Replay: Continuous-Time Mixture Transport for Lifelong Generative Models

Alessandro De Palma, Charles Jones, Tristan Cinquin, Fabian Theis, Frederik Wenkel · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26749 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

以前のタスクを忘れることなく、先に訓練された継続的な時間生成モデルを先行して入力するデータに適応することは、生涯学習の中心的な課題です。 主導的な戦略、生成再生は、歴史的なタスクで訓練された生成モデルを使用して、経験から合成サンプルで自身の継続的な更新を増加させます。 しかし、それは、あらゆるトレーニングステップで完全な軌道シミュレーションを必要とし、スクリプションエラーを蓄積し、再生された歴史的配布を悪化させることができます。 ここで、我々は、継続的なバイオトラフィンのレンズを通じて、我々は、それぞれの密度の密度によって2つのタスクの平均を回避して訓練し、彼らのケースの

Machine translation (Meta M2M100); the English original follows.

Abstract

Adapting pre-trained continuous-time generative models to sequentially incoming data without forgetting earlier tasks is a central challenge in lifelong learning. A leading strategy, generative replay, uses a generative model trained on historical tasks to augment its own continual update with synthetic samples from experience. However, it requires full trajectory simulations at every training step, which accumulate discretization errors and can degrade the replayed historical distribution. Here, we propose trajectory replay, which bypasses sample generation by training an updated velocity field to match both a frozen historical velocity along its trajectories and the incoming task's velocity field, weighted by a stability-plasticity coefficient. Through the lens of continuous-time transport, we show the resulting optimum averages the two task velocities by their respective densities and generates the mixture of their probability paths. Combined with mean velocity distillation, trajectory replay accesses historical trajectories using only a few network evaluations per continual update. Empirically, it outperforms generative replay and distillation baselines on class-incremental image generation. In two biological case studies, trajectory replay extends generative perturbation models to new contexts and experimental batches across two data modalities while preserving treatment-effect prediction.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Trajectory, Computer science, Generative grammar, Generative model, Artificial intelligence, Forgetting

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange