Optimization of Image Embeddings for Few Shot Learning
This work addresses few-shot learning challenges for image classification tasks, but it appears incremental as it modifies existing architectures rather than introducing a new paradigm.
The paper tackled the problem of improving image embeddings for few-shot learning by proposing alternate architectures for existing networks, resulting in outperforming state-of-the-art methods on the Omniglot dataset for 1-shot and 5-shot learning.
In this paper we improve the image embeddings generated in the graph neural network solution for few shot learning. We propose alternate architectures for existing networks such as Inception-Net, U-Net, Attention U-Net, and Squeeze-Net to generate embeddings and increase the accuracy of the models. We improve the quality of embeddings created at the cost of the time taken to generate them. The proposed implementations outperform the existing state of the art methods for 1-shot and 5-shot learning on the Omniglot dataset. The experiments involved a testing set and training set which had no common classes between them. The results for 5-way and 10-way/20-way tests have been tabulated.