Style Generalization
4 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Style Generalization
Latest papers with no code
GenerTTS: Pronunciation Disentanglement for Timbre and Style Generalization in Cross-Lingual Text-to-Speech
Cross-lingual timbre and style generalizable text-to-speech (TTS) aims to synthesize speech with a specific reference timbre or style that is never trained in the target language.
Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias
3) We further use a VQGAN-based acoustic model to generate the spectrogram and a latent code language model to fit the distribution of prosody, since prosody changes quickly over time in a sentence, and language models can capture both local and long-range dependencies.
Domain Generalization for Mammographic Image Analysis with Contrastive Learning
The training of an efficacious deep learning model requires large data with diverse styles and qualities.
Discrepancy-Optimal Meta-Learning for Domain Generalization
This work attempts to tackle the problem of domain generalization (DG) via learning to reduce domain shift with an episodic training procedure.
A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts
To solve these problems, we have introduced a unified and robust multi-modal deep learning architecture which works for English code-mixed dataset and uni-lingual English dataset both. The devised system, uses psycho-linguistic features and very ba-sic linguistic features.
Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning
We show that adversarial learning increases all models generalization capabilities both on manual and automatic speech transcription as well as on encyclopedic data.
Complementary Attributes: A New Clue to Zero-Shot Learning
Extensive experiments on five ZSL benchmark datasets and the large-scale ImageNet dataset demonstrate that the proposed complementary attributes and rank aggregation can significantly and robustly improve existing ZSL methods and achieve the state-of-the-art performance.
(Almost) No Label No Cry
In Learning with Label Proportions (LLP), the objective is to learn a supervised classifier when, instead of labels, only label proportions for bags of observations are known.
