GTC 2020 S21598
Presenters: Meghana Ravikumar,SigOpt
We’ll anchor on building an image classifier trained on the Stanford Cars dataset to evaluate two approaches to transfer learning — fine tuning and feature extraction — and the impact of hyperparameter optimization on these techniques. Once we define the most performant transfer-learning technique for Stanford Cars, we’ll explore Bayesian Optimization as a black-box optimization technique to tune image-transformation parameters required to augment the model, using the downstream image classifier’s performance as the guide. Drawing on a rigorous set of experimental results can help us answer the question: How can resource-constrained teams make tradeoffs between efficiency and effectiveness using pre-trained models?
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