Low-Shot Learning from Imaginary Data (1801.05401v2)

Abstract: Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views. Incorporating this ability to hallucinate novel instances of new concepts might help machine vision systems perform better low-shot learning, i.e., learning concepts from few examples. We present a novel approach to low-shot learning that uses this idea. Our approach builds on recent progress in meta-learning ("learning to learn") by combining a meta-learner with a "hallucinator" that produces additional training examples, and optimizing both models jointly. Our hallucinator can be incorporated into a variety of meta-learners and provides significant gains: up to a 6 point boost in classification accuracy when only a single training example is available, yielding state-of-the-art performance on the challenging ImageNet low-shot classification benchmark.

• The paper introduces a hallucinator-enhanced meta-learning framework that generates synthetic examples to improve low-shot classification.
• It achieves up to a six-point accuracy improvement on the ImageNet low-shot benchmark by integrating both real and generated data.
• The methodology paves the way for more efficient learning from minimal data and inspires future research on diverse hallucinatory mechanisms.

Low-Shot Learning from Imaginary Data

The paper "Low-Shot Learning from Imaginary Data" explores the challenging domain of low-shot learning in computer vision, proposing a novel methodology involving the generation of imaginary training examples. This research builds upon the burgeoning field of meta-learning, known for its potential to enhance learning efficiency by inculcating the ability to "learn to learn." The central proposition is a hallucinator-enhanced meta-learning framework aimed at significantly improving low-shot learning performance.

Methodology

The proposed method integrates a hallucinator with a meta-learning framework. The hallucinator generates additional training examples by simulating human-like imagination, which aids in expanding the dataset when only a limited number of examples are available. This is achieved by feeding both real and generated imaginations into the meta-learner, which is optimized jointly with the hallucinator. The approach contrasts with traditional generative models by focusing explicitly on generating samples that optimize classification performance rather than on realism or diversity.

Results and Evaluation

The paper reports notable performance enhancements with the inclusion of the hallucinator, achieving up to a six-point improvement in classification accuracy for scenarios with only a single training example. These results are validated on the ImageNet low-shot classification benchmark, demonstrating state-of-the-art performance. Moreover, the framework's versatility is emphasized, as it provides significant gains across various meta-learning methods, including prototypical networks and matching networks.

Evaluation protocols include assessments both in restricted label spaces and joint label spaces with novel and base classes. The paper introduces a novel evaluation metric considering the trade-off between base and novel class performance by employing a prior on class probabilities, demonstrating robustness across varying scenarios.

Implications and Future Directions

The research presents significant implications for developing machine learning systems that can efficiently learn from minimal data, mirroring human cognitive abilities. The integration of a hallucinator expands the applicability of meta-learning models, enhancing their practical utility in constrained environments where data acquisition is challenging.

Looking forward, future research could explore improving the diversity of generated examples and extending the approach to other modalities beyond vision. The notion of training a hallucinator using adversarial strategies or exploring novel architectures could be potential avenues to further enhance model performance. As AI advances, the confluence of generative models and meta-learning could lead to increasingly autonomous systems capable of self-directed learning in varied realms.