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Found 2 papers, 1 papers with code
FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content
In this paper, we present FaceChain, a personalized portrait generation framework that combines a series of customized image-generation model and a rich set of face-related perceptual understanding models (\eg, face detection, deep face embedding extraction, and facial attribute recognition), to tackle aforementioned challenges and to generate truthful personalized portraits, with only a handful of portrait images as input.
A New Clustering neural network for Chinese word segmentation
In this article I proposed a new model to achieve Chinese word segmentation(CWS), which may have the potentiality to apply in other domains in the future. It is a new thinking in CWS compared to previous works, to consider it as a clustering problem instead of a labeling problem. In this model, LSTM and self attention structures are used to collect context also sentence level features in every layer, and after several layers, a clustering model is applied to split characters into groups, which are the final segmentation results. I call this model CLNN. This algorithm can reach 98 percent of F score (without OOV words) and 85 percent to 95 percent F score (with OOV words) in training data sets. Error analyses shows that OOV words will greatly reduce performances, which needs a deeper research in the future.
