How do you use unsupervised pre-training to enhance the performance of generative models

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Can you explain, using code, how to use unsupervised pre-training to enhance the performance of generative models?
Nov 12 in Generative AI by Ashutosh
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1 answer to this question.

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You can use unsupervised pre-training to enhance the performance of generative models by referring to the Pytorch implementation below. Where to improve generative models, we are initializing with a strong understanding of patterns before fine-tuning a specific task 

  • Pre-train: The model unsupervised on unlabeled data (e.g., using autoencoders or masked predictions)

The code snippet below explains the pre-trained autoencoder or masked model:

  • Fine-Tune: The pre-trained model on the target task with labeled data(e.g., For conditional generation)
The code snippet below explains the fine-tuned labeled data.
Using the above implementations, you can improve model performance by increasing learned features from the unsupervised phase, speeding up convergence, and enhancing generation quality.

answered Nov 12 by Harsh Yadav

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