Batch-in-Batch: a new adversarial training framework for initial perturbation and sample selection
Abstract Adversarial training methods commonly generate initial perturbations that are independent across epochs, and obtain subsequent adversarial training samples without selection. Consequently, such methods may limit thorough probing of the vicinity around the original samples and possibly lead...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-01-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-024-01704-9 |
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