High-Fidelity Image Generation With Fewer Labels (1903.02271v2)

Abstract: Deep generative models are becoming a cornerstone of modern machine learning. Recent work on conditional generative adversarial networks has shown that learning complex, high-dimensional distributions over natural images is within reach. While the latest models are able to generate high-fidelity, diverse natural images at high resolution, they rely on a vast quantity of labeled data. In this work we demonstrate how one can benefit from recent work on self- and semi-supervised learning to outperform the state of the art on both unsupervised ImageNet synthesis, as well as in the conditional setting. In particular, the proposed approach is able to match the sample quality (as measured by FID) of the current state-of-the-art conditional model BigGAN on ImageNet using only 10% of the labels and outperform it using 20% of the labels.

• The paper demonstrates that high-fidelity image generation can be achieved with as little as 10-20% labeled data through self- and semi-supervised methods.
• Its approach combines pre-trained self-supervised techniques with co-training strategies to extract semantic features that guide GAN training.
• Experimental results on ImageNet show competitive or superior performance compared to BigGAN, significantly reducing reliance on extensive labeled datasets.

High-Fidelity Image Generation with Fewer Labels: A Comprehensive Review

This paper presents an ambitious approach to improving high-fidelity image generation using deep generative models with significantly reduced label requirements. The research leverages recent advances in self-supervised and semi-supervised learning to align or exceed state-of-the-art results on both unsupervised and conditional ImageNet synthesis. The paper's implications are notable in reducing the dependency on large labeled datasets, which typically constrain the training of Generative Adversarial Networks (GANs) to high-fidelity image generation.

Overview of Methods and Contributions

The paper explores several strategies to circumvent the traditional need for extensive labeled data, primarily relying on two core components:

1. Self-Supervised Learning: The paper employs self-supervised learning to extract semantic features from the training data. These features act as a guide in the GAN training process, notably in scenarios void of substantial label availability.
2. Semi-Supervised Learning: The authors infer labels for the entire training dataset from a partially labeled subset. This allows GAN training to incorporate inferred labels as conditional information, greatly reducing the necessity for labeled data.

The key contributions of the work include:

• Establishing state-of-the-art performance in unsupervised image generation on ImageNet.
• Achieving comparable sample quality to BigGAN using only 10% of the labeled samples and surpassing BigGAN with 20% labeled samples.
• Open-sourcing the developed algorithms, thus advancing research transparency and reproducibility in the domain.

Detailed Examination of Techniques

The researchers present both pre-trained and co-training approaches, highlighting practical implementations of these models:

• Pre-trained Approaches: Methods like self-supervised rotation and clustering are employed, forming clusters from learned feature representations without a priori labels. These clusters then guide subsequent GAN training, achieving competitive results.
• Co-training Approach: A methodology that integrates label inference directly within the GAN training regime, improving training efficiency and effectiveness when label data is partially available.

Enhancements through self-supervision during GAN training are extensively evaluated, demonstrating consistent performance improvement across all implementations.

Experimental Setup and Results

Experiments were primarily focused on the ImageNet dataset, resized to 128x128 dimensions. The testing and training regimes employed standard GAN metrics like the Fréchet Inception Distance (FID) and Inception Score (IS). Key results include:

• The unsupervised Clustering-based method reduced FID by about 10% compared to Single-label and Random-label approaches, setting a new benchmark in unsupervised image generation.
• The semi-supervised approach demonstrated proficiency by aligning with or surpassing BigGAN's performance using notably fewer labels.

Implications for Future AI Developments

The findings of this research have profound implications for the scalability and accessibility of GAN models in the field of AI. By reducing the dependency on comprehensive labeled data sets, these methods bolster the potential for wider application in varied domains where labeling is costly or impractical.

The exploration of self-supervised and semi-supervised techniques in conjunction with GANs opens new avenues for developing models that are not only data-efficient but also potentially more resilient to data biases that often plague labeled datasets. Future work may focus on extending these methodologies to more complex and diverse datasets, refining the models for specific applications, and exploring additional enhancements in unsupervised learning methods.

In summary, this paper represents a significant step forward in the optimization of GANs, democratizing access to high-fidelity image generation technologies and laying critical groundwork for future research and application in machine learning.