MORGAN: Meta-Learning-Based Few-Shot Open-Set Recognition via Generative Adversarial Network

Abstract

In few-shot open-set recognition (FSOSR) for hyperspectral images (HSI), one major challenge arises due to the simultaneous presence of spectrally fine-grained known classes and outliers. Prior research on generative FSOSR cannot handle such a situation due to their inability to approximate the open space prudently. To address this issue, we propose a method, Meta-learning-based Open-set Recognition via Generative Adversarial Network (MORGAN), that can learn a finer separation between the closed and the open spaces. MORGAN seeks to generate class-conditioned adversarial samples for both the closed and open spaces in the few-shot regime using two GANs by judiciously tuning noise variance while ensuring discriminability using a novel Anti-Overlap Latent (AOL) regularizer. Adversarial samples from low noise variance amplify known class data density, and we use samples from high noise variance to augment known-unknowns. A first-order episodic strategy is adapted to ensure stability in the GAN training. Finally, we introduce a combination of metric losses which push these augmented known-unknowns or outliers to disperse in the open space while condensing known class distributions. Extensive experiments on four benchmark HSI datasets indicate that MORGAN achieves state-of-the-art FSOSR performance consistently.