Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Problems such as predicting an optical flow field (Y) for an image (X) are ambiguous: many very distinct solutions are good. Representing this ambiguity requires building a conditional model P(Y|X) of the prediction, conditioned on the image. It is hard because training data usually does not contain many different flow fields for the same image. As a result,  need different images to share data to produce good models.  demonstrate an improved method for building conditional models, the Co-Embedding Deep Variational Auto Encoder. Our CDVAE exploits multiple encoding and decoding layers for both X and Y. These are tied during training to produce a model of the joint distribution P(X, Y), which provides the necessary smoothing. Our tying procedure is designed to yield a conditional model easy at test time.  demonstrate our model on three example tasks using real data: image saturation adjustment, image relighting, and motion prediction.  describe quantitative evaluation metrics to evaluate ambiguous generation results. Our results quantitatively and qualitatively advance the state of the art.
['Jiajun Lu', 'Aditya Deshpande', 'David A. Forsyth']
I am writing a research paper on a idea CDVAE: Co-embedding Deep Variational Auto Encoder for Conditional Variational Generation
[{"from": "human", "value": "I am writing a research paper on a idea CDVAE: Co-embedding Deep Variational Auto Encoder for Conditional Variational Generation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Problems such as predicting an optical flow field (Y) for an image (X) are ambiguous: many very distinct solutions are good. Representing this ambiguity requires building a conditional model P(Y|X) of the prediction, conditioned on the image. It is hard because training data usually does not contain many different flow fields for the same image. As a result,  need different images to share data to produce good models.  demonstrate an improved method for building conditional models, the Co-Embedding Deep Variational Auto Encoder. Our CDVAE exploits multiple encoding and decoding layers for both X and Y. These are tied during training to produce a model of the joint distribution P(X, Y), which provides the necessary smoothing. Our tying procedure is designed to yield a conditional model easy at test time.  demonstrate our model on three example tasks using real data: image saturation adjustment, image relighting, and motion prediction.  describe quantitative evaluation metrics to evaluate ambiguous generation results. Our results quantitatively and qualitatively advance the state of the art."}]