Category: Unsupervised
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Finetuning Llama 3 with Odds Ratio Preference Optimization
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Introduction Large Language Models are often trained rather than built, requiring multiple steps to perform well. These steps, including Supervised Fine Tuning (SFT) and Preference Alignment, are crucial for learning new things and aligning with human responses. However, each step takes a significant amount of time and computing resources. One solution is the Odd Ratio…
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How Data Efficient GANs Generate Images of Cats and Dogs?
Introduction Generative adversarial networks are a popular framework for Image generation. In this article we’ll train Data-efficient GANs with Adaptive Discriminator Augmentation that addresses the challenge of limited training data. Adaptive Discriminator Augmentation dynamically adjusts data augmentation during GAN training, preventing discriminator overfitting and enhancing model generalization. By employing invertible augmentation techniques and probabilistic application,…