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Found 10 papers, 5 papers with code
Perturbed examples reveal invariances shared by language models
Possessing a wide variety of invariances may be a key reason for the recent successes of large language models, and our framework can shed light on the types of invariances that are retained by or emerge in new models.
DAD++: Improved Data-free Test Time Adversarial Defense
With the increasing deployment of deep neural networks in safety-critical applications such as self-driving cars, medical imaging, anomaly detection, etc., adversarial robustness has become a crucial concern in the reliability of these networks in real-world scenarios.
What Happens During Finetuning of Vision Transformers: An Invariance Based Investigation
The pretrain-finetune paradigm usually improves downstream performance over training a model from scratch on the same task, becoming commonplace across many areas of machine learning.
Robust Few-shot Learning Without Using any Adversarial Samples
These methods rely on the generation of adversarial samples in every episode of training, which further adds a computational burden.
Data-free Defense of Black Box Models Against Adversarial Attacks
At test time, WNR combined with trained regenerator network is prepended to the black box network, resulting in a high boost in adversarial accuracy.
DE-CROP: Data-efficient Certified Robustness for Pretrained Classifiers
Existing works use this technique to provably secure a pretrained non-robust model by training a custom denoiser network on entire training data.
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems
We, therefore, propose a holistic approach for quantifying adversarial vulnerability of a sample by combining these different perspectives, i. e., degree of model's reliance on high-frequency features and the (conventional) sample-distance to the decision boundary.
DAD: Data-free Adversarial Defense at Test Time
Deep models are highly susceptible to adversarial attacks.
MMD-ReID: A Simple but Effective Solution for Visible-Thermal Person ReID
Learning modality invariant features is central to the problem of Visible-Thermal cross-modal Person Reidentification (VT-ReID), where query and gallery images come from different modalities.
Climate Adaptation: Reliably Predicting from Imbalanced Satellite Data
The utility of aerial imagery (Satellite, Drones) has become an invaluable information source for cross-disciplinary applications, especially for crisis management.
