Jonas Ngnawe

A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy

Yann Batiste Pequignot

Ola Ahmad

Deep learning models operating in the image domain are vulnerable to small input perturbations. For years, robustness to such perturbations … (see more)was pursued by training models from scratch (i.e., with random initializations) using specialized loss ob- jectives. Recently, robust fine-tuning has emerged as a more efficient alternative: instead of training from scratch, pretrained models are adapted to maximize pre- dictive performance and robustness. To conduct robust fine-tuning, practitioners design an optimization strategy that includes the model update protocol (e.g., full or partial) and the specialized loss objective. Additional design choices include the architecture type and size, and the pretrained representation. These design choices affect robust generalization, which is the model’s ability to maintain performance when exposed to new and unseen perturbations at test time. Understanding how these design choices influence generalization remains an open question with signif- icant practical implications. In response, we present an empirical study spanning 6 datasets, 40 pretrained architectures, 2 specialized losses, and 3 adaptation proto- cols — yielding 1, 440 training configurations and 7, 200 robustness measurements across five perturbation types. To our knowledge, this is the most diverse and comprehensive benchmark of robust fine-tuning to date. While attention-based architectures and robust pretrained representations are increasingly popular, we find that convolutional neural networks pretrained in a supervised manner on large datasets often perform best. Our analysis both confirms and challenges prior design assumptions, highlighting promising research directions and offering practical guidance.

2025-09-29

NeurIPS.cc/2025/Workshop/Reliable_ML (published)

openreview.net

A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy

Maxime Heuillet

Rishika Bhagwatkar

Jonas Ngnawe

Yann Batiste Pequignot

Ola Ahmad

Deep learning models operating in the image domain are vulnerable to small input perturbations. For years, robustness to such perturbations … (see more)was pursued by training models from scratch (i.e., with random initializations) using specialized loss objectives. Recently, robust fine-tuning has emerged as a more efficient alternative: instead of training from scratch, pretrained models are adapted to maximize predictive performance and robustness. To conduct robust fine-tuning, practitioners design an optimization strategy that includes the model update protocol (e.g., full or partial) and the specialized loss objective. Additional design choices include the architecture type and size, and the pretrained representation. These design choices affect robust generalization, which is the model's ability to maintain performance when exposed to new and unseen perturbations at test time. Understanding how these design choices influence generalization remains an open question with significant practical implications. In response, we present an empirical study spanning 6 datasets, 40 pretrained architectures, 2 specialized losses, and 3 adaptation protocols, yielding 1,440 training configurations and 7,200 robustness measurements across five perturbation types. To our knowledge, this is the most diverse and comprehensive benchmark of robust fine-tuning to date. While attention-based architectures and robust pretrained representations are increasingly popular, we find that convolutional neural networks pretrained in a supervised manner on large datasets often perform best. Our analysis both confirms and challenges prior design assumptions, highlighting promising research directions and offering practical guidance.

2025-09-29

NeurIPS.cc/2025/Workshop/Reliable_ML (published)

doi.org

openreview.net

Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling

Jonas Ngnawe

Maxime Heuillet

Sabyasachi Sahoo

Yann Batiste Pequignot

Frederic Precioso

Christian Gagné

Fine-tuning pretrained models is the standard approach in current machine learning practice, but simultaneously achieving adversarial robust… (see more)ness to adversarial examples remains a challenge. Despite the abundance of non-robust pretrained models in open-source repositories, their use for Robust Fine-Tuning (RFT) remains understudied. This work aims to bridge this knowledge gap by systematically examining RFT from such models. Our experiments reveal that fine-tuning non-robust models with a robust objective, even under small perturbations, can lead to poor performance, a phenomenon that we dub \emph{suboptimal transfer}. In fact, we find that fine-tuning using a robust objective impedes task alignment at the beginning of training and eventually prevents optimal transfer. To promote optimal transfer, we propose \emph{Epsilon-Scheduling}, a simple heuristic scheduling over perturbation strength. Additionally, we introduce \emph{expected robustness}, a metric that measures performance across a range of perturbations. Experiments on six pretrained models and five datasets show that \emph{Epsilon-Scheduling} prevents \emph{suboptimal transfer} and consistently improves the expected robustness.

2025-09-29

NeurIPS.cc/2025/Workshop/Reliable_ML (published)

openreview.net

Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Adversarial Scheduling

Jonas Ngnawe

Maxime Heuillet

Sabyasachi Sahoo

Yann Batiste Pequignot

Ola Ahmad

Audrey Durand

Frederic Precioso

Christian Gagné

Fine-tuning pretrained models is a standard and effective workflow in modern machine learning. However, robust fine-tuning (RFT), which aims… (see more) to simultaneously achieve adaptation to a downstream task and robustness to adversarial examples, remains challenging. Despite the abundance of non-robust pretrained models in open-source repositories, their potential for RFT is less understood. We address this knowledge gap by systematically examining RFT from such non-robust models. Our experiments reveal that fine-tuning non-robust models with a robust objective, even under small perturbations, can lead to poor performance, a phenomenon that we dub \emph{suboptimal transfer}. In challenging scenarios (eg, difficult tasks, high perturbation), the resulting performance can be so low that it may be considered a transfer failure. We find that fine-tuning using a robust objective impedes task adaptation at the beginning of training and eventually prevents optimal transfer. However, we propose a novel heuristic, \emph{Epsilon-Scheduling}, a schedule over perturbation strength used during training that promotes optimal transfer. Additionally, we introduce \emph{expected robustness}, a metric that captures performance across a range of perturbations, providing a more comprehensive evaluation of the accuracy-robustness trade-off for diverse models at test time. Extensive experiments on a wide range of configurations (six pretrained models and five datasets) show that \emph{Epsilon-Scheduling} successfully prevents \emph{suboptimal transfer} and consistently improves expected robustness.

2025-09-27

ArXiv (preprint)

arxiv.org

Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Adversarial Scheduling

Jonas Ngnawe

Maxime Heuillet

Sabyasachi Sahoo

Yann Batiste Pequignot

Ola Ahmad

Audrey Durand

Frederic Precioso

Christian Gagné

Fine-tuning pretrained models is a standard and effective workflow in modern machine learning. However, robust fine-tuning (RFT), which aims… (see more) to simultaneously achieve adaptation to a downstream task and robustness to adversarial examples, remains challenging. Despite the abundance of non-robust pretrained models in open-source repositories, their potential for RFT is less understood. We address this knowledge gap by systematically examining RFT from such non-robust models. Our experiments reveal that fine-tuning non-robust models with a robust objective, even under small perturbations, can lead to poor performance, a phenomenon that we dub \emph{suboptimal transfer}. In challenging scenarios (eg, difficult tasks, high perturbation), the resulting performance can be so low that it may be considered a transfer failure. We find that fine-tuning using a robust objective impedes task adaptation at the beginning of training and eventually prevents optimal transfer. However, we propose a novel heuristic, \emph{Epsilon-Scheduling}, a schedule over perturbation strength used during training that promotes optimal transfer. Additionally, we introduce \emph{expected robustness}, a metric that captures performance across a range of perturbations, providing a more comprehensive evaluation of the accuracy-robustness trade-off for diverse models at test time. Extensive experiments on a wide range of configurations (six pretrained models and five datasets) show that \emph{Epsilon-Scheduling} successfully prevents \emph{suboptimal transfer} and consistently improves expected robustness.

2025-09-27

ArXiv (preprint)

doi.org

arxiv.org

A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy

Maxime Heuillet

Rishika Bhagwatkar

Jonas Ngnawe

Yann Batiste Pequignot

Ola Ahmad

Deep learning models operating in the image domain are vulnerable to small input perturbations. For years, robustness to such perturbations … (see more)was pursued by training models from scratch (i.e., with random initializations) using specialized loss objectives. Recently, robust fine-tuning has emerged as a more efficient alternative: instead of training from scratch, pretrained models are adapted to maximize predictive performance and robustness. To conduct robust fine-tuning, practitioners design an optimization strategy that includes the model update protocol (e.g., full or partial) and the specialized loss objective. Additional design choices include the architecture type and size, and the pretrained representation. These design choices affect robust generalization, which is the model's ability to maintain performance when exposed to new and unseen perturbations at test time. Understanding how these design choices influence generalization remains an open question with significant practical implications. In response, we present an empirical study spanning 6 datasets, 40 pretrained architectures, 2 specialized losses, and 3 adaptation protocols, yielding 1,440 training configurations and 7,200 robustness measurements across five perturbation types. To our knowledge, this is the most diverse and comprehensive benchmark of robust fine-tuning to date. While attention-based architectures and robust pretrained representations are increasingly popular, we find that convolutional neural networks pretrained in a supervised manner on large datasets often perform best. Our analysis both confirms and challenges prior design assumptions, highlighting promising research directions and offering practical guidance.

2025-08-12

ArXiv (preprint)

doi.org

arxiv.org

A Layer Selection Approach to Test Time Adaptation

Sabyasachi Sahoo

Mostafa ElAraby

Jonas Ngnawe

Yann Batiste Pequignot

Frederic Precioso

Christian Gagné

Test Time Adaptation (TTA) addresses the problem of distribution shift by adapting a pretrained model to a new domain during inference. When… (see more) faced with challenging shifts, most methods collapse and perform worse than the original pretrained model. In this paper, we find that not all layers are equally receptive to the adaptation, and the layers with the most misaligned gradients often cause performance degradation. To address this, we propose GALA, a novel layer selection criterion to identify the most beneficial updates to perform during test time adaptation. This criterion can also filter out unreliable samples with noisy gradients. Its simplicity allows seamless integration with existing TTA loss functions, thereby preventing degradation and focusing adaptation on the most trainable layers. This approach also helps to regularize adaptation to preserve the pretrained features, which are crucial for handling unseen domains. Through extensive experiments, we demonstrate that the proposed layer selection framework improves the performance of existing TTA approaches across multiple datasets, domain shifts, model architectures, and TTA losses.

2025-04-11

Proceedings of the AAAI Conference on Artificial Intelligence (published)

doi.org

openreview.net

A Layer Selection Approach to Test Time Adaptation

Sabyasachi Sahoo

Mostafa ElAraby

Jonas Ngnawe

Yann Batiste Pequignot

Frederic Precioso

Christian Gagné

Test Time Adaptation (TTA) addresses the problem of distribution shift by adapting a pretrained model to a new domain during inference. When… (see more) faced with challenging shifts, most methods collapse and perform worse than the original pretrained model. In this paper, we find that not all layers are equally receptive to the adaptation, and the layers with the most misaligned gradients often cause performance degradation. To address this, we propose GALA, a novel layer selection criterion to identify the most beneficial updates to perform during test time adaptation. This criterion can also filter out unreliable samples with noisy gradients. Its simplicity allows seamless integration with existing TTA loss functions, thereby preventing degradation and focusing adaptation on the most trainable layers. This approach also helps to regularize adaptation to preserve the pretrained features, which are crucial for handling unseen domains. Through extensive experiments, we demonstrate that the proposed layer selection framework improves the performance of existing TTA approaches across multiple datasets, domain shifts, model architectures, and TTA losses.

2024-10-10

NeurIPS.cc/2024/Workshop/FITML (poster)

openreview.net

Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers

Jonas Ngnawe

Sabyasachi Sahoo

Yann Batiste Pequignot

Frederic Precioso

Christian Gagné

2024-09-25

NeurIPS.cc/2024/Conference (poster)

doi.org

openreview.net

Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers

Jonas Ngnaw'e

Sabyasachi Sahoo

Yann Pequignot

Frederic Precioso

Christian Gagné

2024-06-26

ArXiv (preprint)

doi.org

arxiv.org

Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers

Jonas Ngnaw'e

Sabyasachi Sahoo

Yann Batiste Pequignot

Fr'ed'eric Precioso

Christian Gagné

Despite extensive research on adversarial training strategies to improve robustness, the decisions of even the most robust deep learning mod… (see more)els can still be quite sensitive to imperceptible perturbations, creating serious risks when deploying them for high-stakes real-world applications. While detecting such cases may be critical, evaluating a model's vulnerability at a per-instance level using adversarial attacks is computationally too intensive and unsuitable for real-time deployment scenarios. The input space margin is the exact score to detect non-robust samples and is intractable for deep neural networks. This paper introduces the concept of margin consistency -- a property that links the input space margins and the logit margins in robust models -- for efficient detection of vulnerable samples. First, we establish that margin consistency is a necessary and sufficient condition to use a model's logit margin as a score for identifying non-robust samples. Next, through comprehensive empirical analysis of various robustly trained models on CIFAR10 and CIFAR100 datasets, we show that they indicate strong margin consistency with a strong correlation between their input space margins and the logit margins. Then, we show that we can effectively use the logit margin to confidently detect brittle decisions with such models and accurately estimate robust accuracy on an arbitrarily large test set by estimating the input margins only on a small subset. Finally, we address cases where the model is not sufficiently margin-consistent by learning a pseudo-margin from the feature representation. Our findings highlight the potential of leveraging deep representations to efficiently assess adversarial vulnerability in deployment scenarios.

2024-06-26

ArXiv (preprint)

doi.org

arxiv.org

Layerwise Early Stopping for Test Time Adaptation

Sabyasachi Sahoo

Mostafa ElAraby

Jonas Ngnawe

Yann Batiste Pequignot

Frederic Precioso

Christian Gagné

2024-04-04