Overcoming Projection Bias in Generalized Zero-Shot Learning through Parameterized Mahalanobis Distance Learning

Core Concepts

The core message of this work is to address the projection bias problem in generalized zero-shot learning (GZSL) by introducing a parameterized Mahalanobis distance metric to improve classification performance on both seen and unseen classes.

Abstract

The content discusses the problem of projection bias in generalized zero-shot learning (GZSL) and proposes a novel approach to address it. Key highlights: GZSL aims to recognize samples from both seen and unseen classes using only seen class samples for training. However, GZSL methods are prone to bias towards seen classes due to the projection function being learned from seen classes. The authors propose to learn a parameterized Mahalanobis distance metric to counteract the performance degradation caused by projection bias. They extend the VAEGAN architecture with two branches to separately output the projection of samples from seen and unseen classes, enabling more robust distance learning. A novel loss function is introduced to optimize the Mahalanobis distance representation and reduce projection bias. Extensive experiments on four datasets show that the proposed approach outperforms state-of-the-art GZSL techniques with improvements of up to 3.5% on the harmonic mean metric.

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Key Insights Distilled From

Bridging the Projection Gap

by Chong Zhang,... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2309.01390.pdf

Deeper Inquiries

How can the proposed Mahalanobis distance learning framework be extended to other generative model architectures beyond VAEGAN

The proposed Mahalanobis distance learning framework can be extended to other generative model architectures by incorporating the Mahalanobis distance metric into the training process of these models. For instance, in a Variational Autoencoder (VAE) framework, the Mahalanobis distance can be integrated into the loss function to optimize the latent space representation. Similarly, in a Generative Adversarial Network (GAN), the Mahalanobis distance can be used to measure the dissimilarity between generated samples and class prototypes. By adapting the loss functions and training procedures of different generative models, the Mahalanobis distance can be effectively utilized to improve the discrimination between seen and unseen classes in zero-shot learning tasks.

What are the potential limitations of the Mahalanobis distance approach, and how can they be addressed in future work

One potential limitation of the Mahalanobis distance approach is the sensitivity to noise and outliers in the data. To address this limitation, robust estimation techniques can be employed to reduce the impact of outliers on the Mahalanobis distance calculation. Additionally, incorporating regularization techniques or data augmentation methods can help improve the robustness of the Mahalanobis distance metric. Furthermore, exploring ensemble methods that combine multiple Mahalanobis distance metrics or incorporating domain adaptation strategies can enhance the generalization capability of the approach.

How can the insights from this work on projection bias be applied to improve zero-shot learning in other domains beyond computer vision

The insights from this work on projection bias can be applied to improve zero-shot learning in various domains beyond computer vision by addressing similar challenges related to domain shifts and bias towards seen classes. For instance, in natural language processing tasks such as text classification or sentiment analysis, where there is a lack of labeled data for all classes, the concept of projection bias can be mitigated by incorporating Mahalanobis distance learning to better distinguish between known and unknown classes based on semantic representations. By adapting the Mahalanobis distance approach to account for the specific characteristics of different domains, zero-shot learning can be enhanced in a wide range of applications.