arXiv subjects

Wojciech Łapacz

Publications and source records attributed to Wojciech Łapacz.

3 recordsLinked to original sources

arXiv2026

Anchored or Drifting: What Recursive Self-Generation Reveals About Training Data

Large generative models are known to memorize their training data, posing severe privacy risks. Yet, current methods to detect training membership typically rely on the weak signals of a single forward pass. In this work, we find that training samples and unseen (held-out) data follow visibly different trajectories under recursive self-generation -- repeatedly feeding a model's output back as its next input. Held-out samples \emph{drift}: they lose the specifics of the original within a few steps. Training samples stay \emph{anchored}, degrading far more slowly. We show that the membership signal this produces holds across model modalities, architectures and scales, spanning language, diffusion, and autoregressive vision models. Furthermore, these recursive trajectories provide a signal that raises membership inference TPR at $1\%$ FPR for nearly every attack we evaluate, roughly doubling it on the weakest baselines and still improving the strongest, which shows that a model's behavior under recursion carries membership evidence that a single query does not.

cs.LG↗

arXiv2026

Dataset Usage Inference without Shadow Models or Held-out Data

How much of my data was used to train a machine learning model? Dataset Usage Inference (DUI) aims to answer this by estimating what fraction of a dataset contributed to a model's training. However, existing DUI methods rely on assumptions that rarely hold in practice: they require training expensive shadow models to imitate the target model, and they assume access to both known training samples and an in-distribution held-out set confirmed to be absent from training. These conditions make current approaches impractical for modern large models and real data ownership disputes. We introduce a practical DUI framework that removes these constraints. Our method requires neither shadow models nor real held-out data. Instead, it generates synthetic non-member samples, extracts diverse membership signals, and casts DUI as a mixture proportion estimation problem to estimate what share of the candidate dataset was used during training. Experiments on large image generative models show that our method reliably quantifies dataset usage, providing a practical tool for data owners to determine how much of their data was used to train a model.

cs.LG↗

arXiv2024

Exploring the Stability Gap in Continual Learning: The Role of the Classification Head

Continual learning (CL) has emerged as a critical area in machine learning, enabling neural networks to learn from evolving data distributions while mitigating catastrophic forgetting. However, recent research has identified the stability gap -- a phenomenon where models initially lose performance on previously learned tasks before partially recovering during training. Such learning dynamics are contradictory to the intuitive understanding of stability in continual learning where one would expect the performance to degrade gradually instead of rapidly decreasing and then partially recovering later. To better understand and alleviate the stability gap, we investigate it at different levels of the neural network architecture, particularly focusing on the role of the classification head. We introduce the nearest-mean classifier (NMC) as a tool to attribute the influence of the backbone and the classification head on the stability gap. Our experiments demonstrate that NMC not only improves final performance, but also significantly enhances training stability across various continual learning benchmarks, including CIFAR100, ImageNet100, CUB-200, and FGVC Aircrafts. Moreover, we find that NMC also reduces task-recency bias. Our analysis provides new insights into the stability gap and suggests that the primary contributor to this phenomenon is the linear head, rather than the insufficient representation learning.

cs.LG↗