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Found 11 papers, 0 papers with code
Multimodal Sequential Generative Models for Semi-Supervised Language Instruction Following
This paper proposes using multimodal generative models for semi-supervised learning in the instruction following tasks.
Conditional Deep Hierarchical Variational Autoencoder for Voice Conversion
Variational autoencoder-based voice conversion (VAE-VC) has the advantage of requiring only pairs of speeches and speaker labels for training.
Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-RL
Therefore, the meta-RL agent faces the challenge of specifying both the hidden task and states based on small amount of experience.
Information Theoretic Regularization for Learning Global Features by Sequential VAE
However, by analyzing the sequential VAEs from the information theoretic perspective, we can claim that simply maximizing the MI encourages the latent variables to have redundant information and prevents the disentanglement of global and local features.
Stablizing Adversarial Invariance Induction by Discriminator Matching
An adversarial invariance induction (AII) shows its power on this purpose, which maximizes the proxy of the conditional entropy between representations and attributes by adversarial training between an attribute discriminator and feature extractor.
Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization
However, previous domain-invariance-based methods overlooked the underlying dependency of classes on domains, which is responsible for the trade-off between classification accuracy and domain invariance.
Invariant Feature Learning by Attribute Perception Matching
An adversarial feature learning (AFL) is a powerful framework to learn representations invariant to a nuisance attribute, which uses an adversarial game between a feature extractor and a categorical attribute classifier.
Adversarial Feature Learning under Accuracy Constraint for Domain Generalization
Learning domain-invariant representation is a dominant approach for domain generalization.
Domain Generalization via Invariant Representation under Domain-Class Dependency
Learning domain-invariant representation is a dominant approach for domain generalization, where we need to build a classifier that is robust toward domain shifts induced by change of users, acoustic or lighting conditions, etc.
Expressive Speech Synthesis via Modeling Expressions with Variational Autoencoder
Recent advances in neural autoregressive models have improve the performance of speech synthesis (SS).
Neuron as an Agent
Existing multi-agent reinforcement learning (MARL) communication methods have relied on a trusted third party (TTP) to distribute reward to agents, leaving them inapplicable in peer-to-peer environments.
