A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts (1712.01381v3)

Abstract: Most existing zero-shot learning methods consider the problem as a visual semantic embedding one. Given the demonstrated capability of Generative Adversarial Networks(GANs) to generate images, we instead leverage GANs to imagine unseen categories from text descriptions and hence recognize novel classes with no examples being seen. Specifically, we propose a simple yet effective generative model that takes as input noisy text descriptions about an unseen class (e.g.Wikipedia articles) and generates synthesized visual features for this class. With added pseudo data, zero-shot learning is naturally converted to a traditional classification problem. Additionally, to preserve the inter-class discrimination of the generated features, a visual pivot regularization is proposed as an explicit supervision. Unlike previous methods using complex engineered regularizers, our approach can suppress the noise well without additional regularization. Empirically, we show that our method consistently outperforms the state of the art on the largest available benchmarks on Text-based Zero-shot Learning.

• The paper introduces a GAN-based method that synthesizes visual features from noisy textual descriptions for improved zero-shot classification.
• It transforms the zero-shot task into a supervised learning problem using a generator, discriminator, and visual pivot regularization.
• Empirical results on CUB and NAB datasets show accuracy improvements of 6.5% and 5.3% over state-of-the-art methods.

A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts

The research paper titled "A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts" authored by Yizhe Zhu et al., contributes to the field of zero-shot learning (ZSL) by presenting a novel method that leverages Generative Adversarial Networks (GANs) for image generation from textual descriptions. This approach addresses the challenge of recognizing unseen object categories based solely on textual information, notably from noisy sources like Wikipedia articles.

Overview

Zero-shot learning attempts to classify instances from categories that were not represented in the training data. Traditional ZSL methods typically frame this as a visual-semantic embedding challenge, projecting instances into a shared semantic space based on attributes or textual descriptions. However, the described approach diverges by employing GANs to synthesize visual features directly from text, converting the ZSL task into a conventional supervised learning problem.

Methodology

The proposed Generative Adversarial Zero-Shot Learning (GAZSL) framework utilizes a generative model that transforms noisy textual descriptions into plausible visual feature representations of unseen classes. This transformation is accomplished through:

1. Generator Network: It receives semantic inputs from textual descriptions, processes these through additional fully connected layers to reduce noise, and generates synthetic visual features incorporating random variability.
2. Discriminator Network: This network distinguishes between real and synthesized features, and also classifies these features into respective categories without biasing towards seen categories.
3. Visual Pivot Regularization: To ensure inter-class discrimination and prevent noise from deteriorating performance, the generator is regularized using visual pivots, which are carefully crafted to match the expected distribution of real data.

Empirical evaluations demonstrate that this approach outperforms state-of-the-art techniques, notably achieving superior results on established benchmarks such as Caltech UCSD Birds-2011 and North America Birds datasets, for both conventional zero-shot recognition and more challenging generalized zero-shot learning contexts.

Results

On both the Caltech UCSD Birds-2011 (CUB) and North America Birds (NAB) datasets, the proposed method exhibited substantial improvements over baseline approaches. On the standard split settings, it improved accuracy by 6.5% on CUB and 5.3% on NAB over the strongest competing method. The use of GANs enables the synthesis of a sufficient variety of distinctive visual features, crucial for the accurate classification of unseen object categories.

Implications and Speculation

The implications of this research are twofold. Practically, by transforming zero-shot learning into a fully supervised task via feature synthesis, this method simplifies the deployment of ZSL systems in real-world scenarios where semantic descriptions are often the only accessible information for novel classes. Theoretically, this work reinforces the potential of adversarial networks in learning rich semantic-visual mappings under substantial noise, prompting further investigations into GAN-based approaches for tasks requiring high-dimensional data synthesis from abstract features.

Future work could explore extending this approach to different domains, incorporating dynamic updates to model visual pivots based on real-world data, and enhancing the robustness of the synthesized features beyond the current domain-specific datasets.

In conclusion, this research presents a technically persuasive advance in zero-shot learning, indicating a promising direction for converting abstract semantic information into actionable visual representations, thereby bridging the gap between textual descriptions and visual recognition.