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Learning good feature embeddings for images often requires substantialtraining data. As a consequence, in settings where training data is limited(e.g., few-shot and zero-shot learning), we are typically forced to use ageneric feature embedding across various tasks. Ideally, we want to constructfeature embeddings that are tuned for the given task. In this work, we proposeTask-Aware Feature Embedding Networks (TAFE-Nets) to learn how to adapt theimage representation to a new task in a meta learning fashion. Our network iscomposed of a meta learner and a prediction network. Based on a task input, themeta learner generates parameters for the feature layers in the predictionnetwork so that the feature embedding can be accurately adjusted for that task.We show that TAFE-Net is highly effective in generalizing to new tasks orconcepts and evaluate the TAFE-Net on a range of benchmarks in zero-shot andfew-shot learning. Our model matches or exceeds the state-of-the-art on alltasks. In particular, our approach improves the prediction accuracy of unseenattribute-object pairs by 4 to 15 points on the challenging visualattribute-object composition task.

TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning