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8d51cca | 1 2 3 4 5 6 7 8 9 10 11 12 13 | #!/usr/bin/env python3
"""
Evolutionary JEPA Masking Search (Evo-JEPA)
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Uses CMA-ES to evolve optimal masking parameters for I-JEPA self-supervised pretraining.
Fitness is evaluated via kNN accuracy on CIFAR-100 after short pretraining runs.
Paper motivation:
- I-JEPA (arxiv:2301.08243) showed masking params swing accuracy by 45+ points
- FER paper (arxiv:2505.11581) showed evolution produces better representations than SGD
- This combines both: evolving the masking strategy that guides SGD-based JEPA training
"""
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