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Decorrelation Mechanism and Dual Neck Autoencoders for Deep Learning
RPI ID: 2022-018-201 Innovation Summary: A dual-neck autoencoder architecture is introduced to improve feature separation and reduce redundancy in deep learning models. The decorrelation mechanism embedded in the encoder layers enhances generalization by minimizing overlap in learned representations. This design allows for more effective training in...
Published: 3/10/2026   |   Inventor(s): Christopher Wiedeman, Ge Wang
Keywords(s): Adversarial Attacks, Attack Transferability, computer vision, Decorrelation, deep learning
Category(s): Computational Science and Engineering