Search Results for author:
Found 10 papers, 6 papers with code
BigVSAN: Enhancing GAN-based Neural Vocoders with Slicing Adversarial Network
In the literature, it has been demonstrated that slicing adversarial network (SAN), an improved GAN training framework that can find the optimal projection, is effective in the image generation task.
Ranked #2 on
Speech Synthesis
on LibriTTS
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
In this paper, we propose a new training scheme that extends the standard VAE via novel stochastic dequantization and quantization, called stochastically quantized variational autoencoder (SQ-VAE).
Nested Named Entity Recognition via Second-best Sequence Learning and Decoding
When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive.
Ranked #2 on
Nested Mention Recognition
on ACE 2005
Diffiner: A Versatile Diffusion-based Generative Refiner for Speech Enhancement
Although deep neural network (DNN)-based speech enhancement (SE) methods outperform the previous non-DNN-based ones, they often degrade the perceptual quality of generated outputs.
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER
We also find that good demonstration can save many labeled examples and consistency in demonstration contributes to better performance.
SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer
Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives.
Ranked #1 on
Image Generation
on FFHQ 1024 x 1024
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models
Explanation-based model debugging aims to resolve spurious biases by showing human users explanations of model behavior, asking users to give feedback on the behavior, then using the feedback to update the model.
Diffusion-Based Speech Enhancement with Joint Generative and Predictive Decoders
At the decoded feature level, we fuse the two decoded features by generative and predictive decoders.
On the Language Encoder of Contrastive Cross-modal Models
Contrastive cross-modal models such as CLIP and CLAP aid various vision-language (VL) and audio-language (AL) tasks.
HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes
Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations.
