Optimization Encoders: Rethinking Second-Order Meta-Learning for Neural Fields

Excerpt

arXiv:2610.08075v1 Announce Type: new Abstract: Conditional neural fields represent signals continuously, but their effectiveness depends on how the conditional latent representations are inferred from observed data. In meta-learning, this encoding occurs through gradient updates induced by the decoder, tying representation learning directly to decoder design. We formalize this connection by interpreting latent optimization as an optimization encoder, unifying the roles of second-order different