Feature Generating Networks for Zero-Shot Learning (1712.00981v2)

Abstract: Suffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning task. To circumvent the need for labeled examples of unseen classes, we propose a novel generative adversarial network (GAN) that synthesizes CNN features conditioned on class-level semantic information, offering a shortcut directly from a semantic descriptor of a class to a class-conditional feature distribution. Our proposed approach, pairing a Wasserstein GAN with a classification loss, is able to generate sufficiently discriminative CNN features to train softmax classifiers or any multimodal embedding method. Our experimental results demonstrate a significant boost in accuracy over the state of the art on five challenging datasets -- CUB, FLO, SUN, AWA and ImageNet -- in both the zero-shot learning and generalized zero-shot learning settings.

• The paper introduces f-CLSWGAN, a GAN-based model that synthesizes discriminative CNN features for both seen and unseen classes.
• It significantly improves zero-shot and generalized zero-shot learning accuracies across benchmarks like CUB and FLO.
• The model enables flexible classifier training and sets a new standard for evaluating generative approaches in ZSL tasks.

Feature Generating Networks for Zero-Shot Learning

The paper "Feature Generating Networks for Zero-Shot Learning" presents a novel approach aimed at addressing the extreme data imbalance between seen and unseen classes in zero-shot learning (ZSL) and generalized zero-shot learning (GZSL) tasks. This imbalance poses significant challenges for existing state-of-the-art methods. The authors propose a solution through the use of generative adversarial networks (GANs) to synthesize CNN features conditioned on class-level semantic information, which enables the training of classifiers without requiring labeled examples from unseen classes.

Core Contributions

The paper makes several key contributions:

1. Novel GAN Architecture: The authors introduce f-CLSWGAN, a conditional Wasserstein GAN combined with a classification loss. This model synthesizes discriminative CNN features for both seen and unseen classes.
2. Robust Performance: The model demonstrated superior accuracy across various ZSL and GZSL benchmarks, including CUB, FLO, SUN, AWA, and ImageNet.
3. Generality and Flexibility: The proposed method is adaptable across different deep CNN features (e.g., GoogleNet, ResNet) and can incorporate various types of class-level auxiliary information (e.g., attributes, sentences, word embeddings).

Experimental Results

The empirical results support the efficacy of the proposed approach. The f-CLSWGAN model outperformed previous state-of-the-art models significantly in both ZSL and GZSL settings. For example, on the CUB dataset, the harmonic mean (H) measure improved from 34.4% to 49.7% in the GZSL setting. Similarly, on the FLO dataset, the ZSL top-1 accuracy increased from 53.4% to 71.2%.

Methodology Insights

Feature Generation

f-CLSWGAN Architecture:

• Generator (G): Takes random noise and class embeddings to generate synthetic CNN features.
• Discriminator (D): Distinguishes between real and generated features, optimizing the Wasserstein distance along with a gradient penalty.
• Classification Loss: Enhances the generator by enforcing the generated features to be discriminative.

The authors compared several GAN variants including f-GAN, f-WGAN, and f-CLSWGAN, finding that f-CLSWGAN consistently performed the best. Additionally, the model’s robustness was evaluated on different deep CNN architectures and class embeddings, demonstrating its general applicability.

Comparative Analysis

The paper includes comparisons with various baseline methods:

• DEVISE, SJE, LATEM, ESZSL, ALE: The proposed feature generation approach improved upon these methods by integrating synthetic CNN features, leading to better balanced and higher accuracies in GZSL settings.
• Softmax Classifier: The flexibility of f-CLSWGAN enabled the use of a softmax classifier, which was previously infeasible for GZSL tasks with non-synthetic data.

Practical and Theoretical Implications

The introduction of f-CLSWGAN has notable implications both practically and theoretically:

1. Practical Impact:
   • Data Imbalance Mitigation: By generating high-quality unseen class features, the model addresses the common issue of data imbalance in practical deployment scenarios.
   • Flexible Classifier Training: It enables the training of various classifiers, including simple ones like softmax, which can now be used effectively in GZSL tasks.
2. Theoretical Insights:
   • Evaluation of Generative Models: The authors propose using GZSL tasks to evaluate generative models, providing an objective and quantitative measure of a generative model’s performance.
   • Generative Model Comparisons: The experimental results indicate that GAN-based feature generation aligns with improvements seen through qualitative metrics, such as inception scores in image generation tasks.

Future Developments

Future research may explore several directions building upon this work:

• Enhanced Class Embeddings: Utilizing more sophisticated or hybrid class embeddings could further improve feature generation quality.
• Scalability and Efficiency: Optimizing the computational efficiency of GAN training for large-scale datasets could enhance practical deployment.
• Cross-domain Applications: Extending this approach to cross-domain ZSL tasks where the attributes or semantic information can significantly vary between domains.

In conclusion, this work represents an important advancement in zero-shot learning by leveraging generative adversarial networks to address training data imbalances, thereby enhancing classifier performance on unseen classes. The generalizable nature of f-CLSWGAN and its robust empirical performance underscore its potential for wide applicability in practical AI systems.