Navigating Hidden Depths: Uncovering the Secrets of Neural Architecture Search

BOIL updates the parameters of the inner loop, excluding the final layer, while freezing all outer loop parameters. This approach encourages the graph convolutional network (GCN) to learn new features when encountering unseen data instead of reusing old ones. By doing so, BOIL can adapt to new domains more effectively than existing NAS methods.
Analogies: Think of BOIL as a chef who wants to create a perfect dish without reinventing the wheel every time. Instead of starting from scratch each time, the chef uses a modified recipe book that only needs minor adjustments for new meals. Similarly, BOIL adapts the neural network architecture by making small modifications to the inner loop parameters, allowing it to learn new features more efficiently.
Performance Comparison: In experiments conducted on several datasets, BOIL outperformed other NAS methods, including MAML and ANIL. By updating only the inner loop parameters, BOIL can avoid overfitting and improve overall performance in new domains.
Conclusion: In summary, BOIL offers a promising solution for efficient neural architecture search by leveraging body-only meta learning. By adapting to new domains more effectively, BOIL can improve out-of-domain generalization and provide better performance. With its simplicity and efficiency, BOIL has the potential to revolutionize the field of deep learning once again.