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Found 13 papers, 4 papers with code
Improve Training Stability of Semi-supervised Generative Adversarial Networks with Collaborative Training
Improved generative adversarial network (Improved GAN) is a successful method of using generative adversarial models to solve the problem of semi-supervised learning.
A Self-Training Method for Semi-Supervised GANs
In this work, we combine these two ideas and make GANs self-trainable for semi-supervised learning tasks by exploiting their infinite data generation potential.
Chunk-Based Bi-Scale Decoder for Neural Machine Translation
In typical neural machine translation~(NMT), the decoder generates a sentence word by word, packing all linguistic granularities in the same time-scale of RNN.
Neural Machine Translation with Reconstruction
Although end-to-end Neural Machine Translation (NMT) has achieved remarkable progress in the past two years, it suffers from a major drawback: translations generated by NMT systems often lack of adequacy.
Context Gates for Neural Machine Translation
In neural machine translation (NMT), generation of a target word depends on both source and target contexts.
Modeling Coverage for Neural Machine Translation
Attention mechanism has enhanced state-of-the-art Neural Machine Translation (NMT) by jointly learning to align and translate.
