Multi-modal Cycle-consistent Generalized Zero-Shot Learning (1808.00136v2)

Abstract: In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes and the visual representations of only the seen classes, while testing uses the visual representations of the seen and unseen classes. Current methods address GZSL by learning a transformation from the visual to the semantic space, exploring the assumption that the distribution of classes in the semantic and visual spaces is relatively similar. Such methods tend to transform unseen testing visual representations into one of the seen classes' semantic features instead of the semantic features of the correct unseen class, resulting in low accuracy GZSL classification. Recently, generative adversarial networks (GAN) have been explored to synthesize visual representations of the unseen classes from their semantic features - the synthesized representations of the seen and unseen classes are then used to train the GZSL classifier. This approach has been shown to boost GZSL classification accuracy, however, there is no guarantee that synthetic visual representations can generate back their semantic feature in a multi-modal cycle-consistent manner. This constraint can result in synthetic visual representations that do not represent well their semantic features. In this paper, we propose the use of such constraint based on a new regularization for the GAN training that forces the generated visual features to reconstruct their original semantic features. Once our model is trained with this multi-modal cycle-consistent semantic compatibility, we can then synthesize more representative visual representations for the seen and, more importantly, for the unseen classes. Our proposed approach shows the best GZSL classification results in the field in several publicly available datasets.

Insights on Multi-modal Cycle-consistent Generalized Zero-Shot Learning

The paper "Multi-modal Cycle-consistent Generalized Zero-Shot Learning" by Rafael Felix et al., investigates advancements in Generalized Zero Shot Learning (GZSL) using Generative Adversarial Networks (GANs). Existing GZSL methodologies often suffer from low accuracy due to their propensity to transform unseen visual representations into seen classes' semantic features. The authors introduce a novel multi-modal cycle-consistency constraint aimed at improving the accuracy by ensuring that synthetic visual representations map back to their original semantic features.

Core Contributions

The central proposition of the paper is a regularization technique applied during the GAN training, which mandates the generated visual features to reconstruct their original semantic features. This cycle-consistency loss is inspired by techniques used in CycleGANs to enforce a more constrained optimization process. Consequently, this approach promises to yield synthesized visual representations that better reflect their semantic features, vital for effective GZSL classification.

Methodological Advancements

1. Cycle-consistent GAN:
   • A key innovation is the integration of a multi-modal cycle-consistency loss within the GAN framework, which minimizes discrepancies between original and reconstructed semantic features.
   • The generator $G$ synthesizes visual representations using a semantic feature and a noise vector, while the discriminator $D$ discerns real from generated visuals.
   • The regressor $R$ maps synthesized visual features back to semantic features, governed by the cycle-consistency loss.
2. Training and Testing:
   • The training leverages synthetic samples of both seen and unseen classes by applying the newly formulated loss in conjunction with existing WGAN objectives.
   • Testing involves evaluating synthetic samples through a softmax classifier, with top-1 accuracy analyzed across multiple benchmark datasets (CUB, FLO, SUN, AWA, and ImageNet).

Experimental Validation

The experimental results underscore significant performance enhancements with the proposed framework compared to baseline models like f-CLSWGAN. The algorithm's ability to better handle unseen class distributions manifests in superior top-1 accuracy and harmonic mean results across GZSL and ZSL evaluations on widely-recognized datasets.

• GZSL Performance: The cycle-consistent approaches like cycle-(U)WGAN exhibited improvements in unseen class accuracy and overall harmonic means. Particularly on datasets like CUB and FLO, marked advances were noted, thereby establishing a new benchmark for GZSL accuracy.
• ZSL Performance: Consistent enhancements were observed in zero-shot evaluations, confirming the merits of enforcing semantic consistency during synthetic visual representation generation.

Implications and Future Directions

The introduction of cycle-consistency into GZSL frameworks is a promising step towards reducing the bias towards seen classes, a common pitfall in zero-shot scenarios. By ensuring semantic compatibility, the synthesized samples offer more reliable training data, potentially leading to robust real-world applications where labeled data is limited.

Future exploration should delve into optimizing GAN stability for large-scale implementations and exploring alternative regularization techniques that can further bridge the gap between synthetic and real data distributions. Addressing these dimensions will be crucial in broadening the applicability of zero-shot learning models in diverse domains and enhancing their efficacy in inherently unconstrained environments.