Method and apparatus for training unsupervised conditional generative model
AssigneeSeoul National University R&Db Foundation
InventorsSungroh Yoon, Uiwon Hwang, Heeseung Kim
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Abstract
Disclosed is a method of training a generative model capable of robustly training the generative model even when there is no label in training data, salient attributes are uneven, and there is only a small number of pieces of data labeled with a salient attribute desired to be learned, and is a method of training an unsupervised conditional generative model using a method of learning parameters of a latent distribution, a generative model, and an encoder.
Claims20
A processor-implemented method of training an unsupervised conditional generative model, the method comprising:
- defining distributions of a plurality of components including mean vectors, respectively, and sampling a latent vector from a latent distribution including the distributions of the plurality of components;
- generating synthetic data using the latent vector as input of the generative model;
- inputting the synthetic data to an encoder to acquire an encoding vector;
- training the generative model and the encoder based on a value of a loss function configured to make the synthetic data closer to real data,
- wherein the loss function includes a generative model loss function and an unsupervised conditional contrastive loss function, and the unsupervised conditional contrastive loss function is configured so that the encoding vector is closer to a mean vector of one component determined based on responsibility with the latent vector among the plurality of components and farther from mean vectors of the other components;
- and
- redetermining parameters of the latent distribution based on the value of the loss function,
- wherein the parameters of the latent distribution include mixing coefficients for the plurality of component distributions, and the redetermining of the parameters comprises redetermining parameters for the plurality of component distributions and the mixing coefficients based on an estimated gradient of the loss function.
An apparatus for training an unsupervised conditional generative model, the apparatus comprising:
- a processor;
- and
- a memory operably connected to the processor to store at least one piece of code executed by the processor,
- wherein, when executed by the processor, the memory stores code causing the processor to:
- define distributions of a plurality of components including mean vectors, respectively, and sample a latent vector from a latent distribution including the distributions of the plurality of components;
- generate synthetic data using the latent vector as input of the generative model;
- input the synthetic data to an encoder to acquire an encoding vector;
- train the generative model and the encoder based on a value of a loss function configured to make the synthetic data closer to real data,
- wherein the loss function includes a generative model loss function and an unsupervised conditional contrastive loss function, and the unsupervised conditional contrastive loss function is configured so that the encoding vector is closer to a mean vector of one component determined based on responsibility with the latent vector among the plurality of components and farther from mean vectors of the other components;
- and
- redetermine parameters of the latent distribution based on the value of the loss function,
- wherein the parameters of the latent distribution include mixing coefficients for the plurality of component distributions, and the redetermining of the parameters comprises redetermining parameters for the plurality of component distributions and the mixing coefficients based on an estimated gradient of the loss function.
Dependencies are inferred from the claim text.