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Prototypical Model with Novel Information-theoretic Loss Function for Generalized Zero Shot Learning
Then We propose three information-theoretic loss functions for deterministic GZSL model: a mutual information loss to bridge seen data and target classes; an uncertainty-aware entropy constraint loss to prevent overfitting when using seen data to learn the embedding of target classes; a semantic preserving cross entropy loss to preserve the semantic relation when mapping the semantic representations to the common space.
Stochastic Variational Inference via Upper Bound
Recently various divergences have been proposed to design the surrogate loss for variational inference.
Sequential Learning for Dirichlet Process Mixtures
We propose an evidence upper bound (EUBO) to act as the surrogate loss, and fit a DP mixture to the given data by minimizing the EUBO, which is equivalent to minimizing the KL-divergence between the target distribution and the DP mixture.
Stein Variational Gradient Descent for Approximate Bayesian Computation
Approximate Bayesian Computation (ABC) provides a generic framework of Bayesian inference for likelihood-free models, but sampling based posterior approximation is often time-consuming and has difficulty accessing the convergence.
