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TAILS resolves cross-task ambiguities in continual learning by directly correcting feature representations, leading to significant performance boosts without changing the underlying model.
CustomShift achieves unprecedented balance between semantic fidelity and subject consistency in image generation, outperforming existing methods by leveraging a novel attention distribution shift.
Batch normalization's power comes from reshaping the geometry of neural network decision boundaries on a per-batch basis, not just from optimization benefits.
