Latte performs seed-centered one-step latent mutations along class anchors in VQ-VAE space to produce diverse, low-drift, fault-revealing DNN tests.
Deepxplore: Automated whitebox testing of deep learning systems,
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CAFD improves DNN fault detection by 18.3% on average using a novel concept-based feature from VLMs alongside other signals, outperforming five baselines on multiple models and datasets.
BayesWarp is a testing framework combining saliency techniques and uncertainty-aware Bayesian optimization to discover diverse model failures on image classification tasks while preserving data proximity.
citing papers explorer
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Latent Anchor-Driven Test Generation for Deep Neural Networks
Latte performs seed-centered one-step latent mutations along class anchors in VQ-VAE space to produce diverse, low-drift, fault-revealing DNN tests.
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CAFD: Concept-Aware DNN Fault Detection using VLMs
CAFD improves DNN fault detection by 18.3% on average using a novel concept-based feature from VLMs alongside other signals, outperforming five baselines on multiple models and datasets.
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Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes
BayesWarp is a testing framework combining saliency techniques and uncertainty-aware Bayesian optimization to discover diverse model failures on image classification tasks while preserving data proximity.