Introduces Targeted Downstream-Agnostic Attack (TDAA) that uses a threat image as feature anchor and example-specific perturbations to achieve targeted attacks on unknown downstream tasks from pre-trained encoders.
Learning multiple layers of features from tiny images,
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Enforcing feature decorrelation during training produces sharper saliency maps and higher accuracy on image classification benchmarks.
A complete pipeline for federated unlearning via knowledge distillation for efficient removal and a GAN-integrated classifier for visual evaluation of forgetting capacity.
Intelligence Inertia models the computational resistance to structural change in neural networks via a heuristic relativistic analogy, yielding a J-shaped cost curve that diverges from classical approximations.
citing papers explorer
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Targeted Downstream-Agnostic Attack
Introduces Targeted Downstream-Agnostic Attack (TDAA) that uses a threat image as feature anchor and example-specific perturbations to achieve targeted attacks on unknown downstream tasks from pre-trained encoders.
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SaliencyDecor: Enhancing Neural Network Interpretability through Feature Decorrelation
Enforcing feature decorrelation during training produces sharper saliency maps and higher accuracy on image classification benchmarks.
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Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation
A complete pipeline for federated unlearning via knowledge distillation for efficient removal and a GAN-integrated classifier for visual evaluation of forgetting capacity.
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Intelligence Inertia: Physical Isomorphism and Applications
Intelligence Inertia models the computational resistance to structural change in neural networks via a heuristic relativistic analogy, yielding a J-shaped cost curve that diverges from classical approximations.