10 citations · 13 across the 3 of their papers we have counts for
3 papers
cs.CL2022
An Overview on Controllable Text Generation via Variational Auto-Encoders
Haoqin Tu, Yitong Li
Recent advances in neural-based generative modeling have reignited the hopes of having computer systems capable of conversing with humans and able to understand natural language. T…
cs.CL2022★ 10 cited
PCAE: A Framework of Plug-in Conditional Auto-Encoder for Controllable Text Generation
Haoqin Tu, Zhongliang Yang, Jinshuai Yang +2
Controllable text generation has taken a gigantic step forward these days. Yet existing methods are either constrained in a one-off pattern or not efficient enough for receiving mu…
cs.CV2021★ 3 cited
Pixel-Stega: Generative Image Steganography Based on Autoregressive Models
Siyu Zhang, Zhongliang Yang, Haoqin Tu +2
In this letter, we explored generative image steganography based on autoregressive models. We proposed Pixel-Stega, which implements pixel-level information hiding with autoregress…