58 citations · 407 across the 33 of their papers we have counts for
6 papers · 1 filter
Multi-Reference Neural TTS Stylization with Adversarial Cycle Consistency
Matt Whitehill, Shuang Ma, Daniel McDuff +1
Current multi-reference style transfer models for Text-to-Speech (TTS) perform sub-optimally on disjoints datasets, where one dataset contains only a single style class for one of…
Unpaired Image-to-Speech Synthesis with Multimodal Information Bottleneck
Shuang Ma, Daniel McDuff, Yale Song
Deep generative models have led to significant advances in cross-modal generation such as text-to-image synthesis. Training these models typically requires paired data with direct…
Image to Video Domain Adaptation Using Web Supervision
Andrew Kae, Yale Song
Training deep neural networks typically requires large amounts of labeled data which may be scarce or expensive to obtain for a particular target domain. As an alternative, we can…
Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval
Yale Song, Mohammad Soleymani
Visual-semantic embedding aims to find a shared latent space where related visual and textual instances are close to each other. Most current methods learn injective embedding func…
M3D-GAN: Multi-Modal Multi-Domain Translation with Universal Attention
Shuang Ma, Daniel McDuff, Yale Song
Generative adversarial networks have led to significant advances in cross-modal/domain translation. However, typically these networks are designed for a specific task (e.g., dialog…
Characterizing Bias in Classifiers using Generative Models
Daniel McDuff, Shuang Ma, Yale Song +1
Models that are learned from real-world data are often biased because the data used to train them is biased. This can propagate systemic human biases that exist and ultimately lead…