6 citations · 7 across the 4 of their papers we have counts for
5 papers
Do we need Label Regularization to Fine-tune Pre-trained Language Models?
Ivan Kobyzev, Aref Jafari, Mehdi Rezagholizadeh +5
Knowledge Distillation (KD) is a prominent neural model compression technique that heavily relies on teacher network predictions to guide the training of a student model. Consideri…
From Unsupervised Machine Translation To Adversarial Text Generation
Ahmad Rashid, Alan Do-Omri, Md. Akmal Haidar +2
We present a self-attention based bilingual adversarial text generator (B-GAN) which can learn to generate text from the encoder representation of an unsupervised neural machine tr…
Bilingual-GAN: A Step Towards Parallel Text Generation
Ahmad Rashid, Alan Do-Omri, Md. Akmal Haidar +2
Latent space based GAN methods and attention based sequence to sequence models have achieved impressive results in text generation and unsupervised machine translation respectively…
Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation
Md. Akmal Haidar, Mehdi Rezagholizadeh, Alan Do-Omri +1
Text generation with generative adversarial networks (GANs) can be divided into the text-based and code-based categories according to the type of signals used for discrimination. I…
A Self-Training Method for Semi-Supervised GANs
Alan Do-Omri, Dalei Wu, Xiaohua Liu
Since the creation of Generative Adversarial Networks (GANs), much work has been done to improve their training stability, their generated image quality, their range of application…