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Found 5 papers, 2 papers with code
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders
While variational autoencoders (VAEs) have been widely applied in text generation tasks, they are troubled by two challenges: insufficient representation capacity and poor controllability.
Rethinking The Uniformity Metric in Self-Supervised Learning
Uniformity plays a crucial role in the assessment of learned representations, contributing to a deeper comprehension of self-supervised learning.
Discrete Auto-regressive Variational Attention Models for Text Modeling
We further design discrete latent space for the variational attention and mathematically show that our model is free from posterior collapse.
Discrete Variational Attention Models for Language Generation
Variational autoencoders have been widely applied for natural language generation, however, there are two long-standing problems: information under-representation and posterior collapse.
DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification
In this paper, we propose a new unsupervised domain adaptation method named Domain-Adversarial Residual-Transfer (DART) learning of Deep Neural Networks to tackle cross-domain image classification tasks.
