UniGeo unifies geometric guidance across three levels in video models to reduce geometric drift and improve consistency in camera-controllable image editing.
MIT Press (2016),http: //www.deeplearningbook.org
2 Pith papers cite this work. Polarity classification is still indexing.
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DNN distinguishers detect no exploitable patterns in ML-KEM, BIKE, HQC, RSA hybrids, or AES/ChaCha20/DES cascades, consistent with IND-CPA security.
citing papers explorer
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UniGeo: Unifying Geometric Guidance for Camera-Controllable Image Editing via Video Models
UniGeo unifies geometric guidance across three levels in video models to reduce geometric drift and improve consistency in camera-controllable image editing.
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Evaluating PQC KEMs, Combiners, and Cascade Encryption via Adaptive IND-CPA Testing Using Deep Learning
DNN distinguishers detect no exploitable patterns in ML-KEM, BIKE, HQC, RSA hybrids, or AES/ChaCha20/DES cascades, consistent with IND-CPA security.