Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions (1812.03664v6)

Abstract: Learning with limited data is a key challenge for visual recognition. Many few-shot learning methods address this challenge by learning an instance embedding function from seen classes and apply the function to instances from unseen classes with limited labels. This style of transfer learning is task-agnostic: the embedding function is not learned optimally discriminative with respect to the unseen classes, where discerning among them leads to the target task. In this paper, we propose a novel approach to adapt the instance embeddings to the target classification task with a set-to-set function, yielding embeddings that are task-specific and are discriminative. We empirically investigated various instantiations of such set-to-set functions and observed the Transformer is most effective -- as it naturally satisfies key properties of our desired model. We denote this model as FEAT (few-shot embedding adaptation w/ Transformer) and validate it on both the standard few-shot classification benchmark and four extended few-shot learning settings with essential use cases, i.e., cross-domain, transductive, generalized few-shot learning, and low-shot learning. It archived consistent improvements over baseline models as well as previous methods and established the new state-of-the-art results on two benchmarks.

• The paper introduces a task-specific embedding adaptation method using set-to-set functions to enhance discrimination for unseen classes.
• The transformer-based FEAT model significantly outperforms traditional few-shot classifiers on benchmarks like MiniImageNet and TieredImageNet.
• The study lays a foundation for robust few-shot learning applications in data-scarce scenarios, with implications for diverse AI domains.

Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions

The paper presents an innovative approach to few-shot learning by employing a set-to-set transformation for embedding adaptation. Recognizing the limitations of task-agnostic instance embeddings which do not optimally differentiate unseen classes, the authors propose a method to adapt these embeddings specifically for target tasks. This adaptation is achieved through the use of set-to-set functions, with the transformer model (FEAT) proving to be the most effective among various implementations.

Key Contributions

1. Task-Specific Embedding Adaptation: The paper introduces a methodology for adapting embeddings to target tasks using set-to-set functions, thereby enhancing the discriminative capabilities of embeddings for few-shot classification tasks. This approach seeks to overcome the core limitation of assuming a common embedding space that may not effectively transfer to new unseen classes.
2. Transformer-Based Set-to-Set Function: Through empirical evaluations, the transformer-based implementation (FEAT) of the set-to-set transformation function exhibited superior performance in few-shot learning tasks. The transformers naturally possess properties critical for set transformations, including contextualization and permutation invariance.
3. Comprehensive Benchmarks and Evaluations: The paper demonstrates the efficacy of the FEAT model across standard few-shot learning benchmarks and additional settings such as cross-domain, transductive, and generalized few-shot learning tasks. The results indicate a consistent improvement over existing baseline models and established new state-of-the-art results on certain benchmarks.

Strong Numerical Results

The FEAT model shows a significant enhancement in performance across various few-shot settings. For instance, on the MiniImageNet dataset, the model achieves 55.15% in the 1-shot 5-way classification task, significantly outperforming traditional methods like ProtoNet and recent techniques within the same framework. Similarly, on TieredImageNet, FEAT also leads in performance metrics, reinforcing its robustness and adaptability.

Implications

Practical Impact: The ability to adapt embedding spaces to specific tasks can considerably enhance few-shot learning applications, potentially leading to improved implementations in scenarios with limited training data, such as medical imaging and other real-world applications where data labeling is cost-prohibitive.

Theoretical Contributions: The introduction of a set-to-set transformation function within few-shot learning paradigms opens new research directions on embedding adaptation, particularly how transformers can be employed in similar tasks beyond visual recognition, broadening the application of few-shot learning models.

Future Directions

The success of the FEAT model provides a foundation for further exploration into transformer dynamics in embedding adaptation. Future research may explore optimizing multi-head and multi-layer transformers for even more expressive adaptations, expanding this framework to other modalities like text or audio. Additionally, investigating more robust regularization methods to mitigate overfitting in deeper layers could potentially yield further gains.

The paper’s contribution lies in its advanced embedding adaptation mechanism, providing insightful progress in the few-shot learning paradigm and marking a valuable intersection of transformer architectures and few-shot tasks, setting a new path for future innovations in AI.