Code Division Modulation Layers Against Forgetting and Inference in Continual Gait Identification

arXiv:2607.19122 · cs.MM, cs.AI, cs.CR · Submitted 2026-07-21 · Read on arXiv

Simone Milani

cs.MM, cs.AI, cs.CR

Submitted: 2026-07-21

Comments: Submitted to EUSIPCO 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 20× in this paper · explained in 1 episode
• Continual Learning — 17× in this paper · explained in 2 episodes
• Catastrophic Forgetting — 10× in this paper · explained in 5 episodes
• Membership Inference Attacks — 6× in this paper
• AUC — 4× in this paper · explained in 2 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Code Division Modulating Layer — 2× in this paper
• Differential Privacy — 2× in this paper · explained in 4 episodes
• Equal Error Rate — 2× in this paper
• Federated Learning — 2× in this paper · explained in 15 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Area Under Curve — 1× in this paper
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Identity Inference Attacks — 1× in this paper
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

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