Precise Recovery of Latent Vectors from Generative Adversarial Networks (1702.04782v2)

Abstract: Generative adversarial networks (GANs) transform latent vectors into visually plausible images. It is generally thought that the original GAN formulation gives no out-of-the-box method to reverse the mapping, projecting images back into latent space. We introduce a simple, gradient-based technique called stochastic clipping. In experiments, for images generated by the GAN, we precisely recover their latent vector pre-images 100% of the time. Additional experiments demonstrate that this method is robust to noise. Finally, we show that even for unseen images, our method appears to recover unique encodings.

Precise Recovery of Latent Vectors from Generative Adversarial Networks

The paper "Precise Recovery of Latent Vectors from Generative Adversarial Networks" by Zachary C. Lipton and Subarna Tripathi addresses the challenge of inverting the mappings of generative adversarial networks (GANs). While GANs are well-established for generating images from latent vectors, the reverse process—projecting image space back into latent space—has remained an open question. This research introduces a method termed "stochastic clipping," demonstrating its efficacy in recovering latent vectors with high precision.

Methodology

The researchers propose a gradient-based approach for reconstructing latent vectors, employing the optimisation of neural network loss surfaces. The core innovation is the concept of stochastic clipping, a modification of standard clipping that enhances the accuracy and robustness of reconstructions. The method is empirically validated by reconstructing latent inputs to arbitrary precision using a Deep Convolutional GAN (DCGAN).

Key Findings

1. Precise Inversion: The stochastic clipping technique achieves a 100% success rate in recovering the true latent vector with arbitrary precision across 1000 experiments. This method proves effective in bypassing local minima—a significant challenge in non-convex optimisation problems.
2. Robustness to Noise: Experiments adding Gaussian noise to images show that stochastic clipping maintains low reconstruction error in latent space. This robustness indicates the technique's utility in scenarios involving noisy data.
3. Consistency in Unseen Images: For images not seen during training, the approach consistently recovers unique vector representations. This finding suggests the potential for the method to provide stable encodings for novel inputs.

Implications and Future Directions

The results offer significant implications for the fields of image reconstruction and understanding the internal representations of GANs. The technique could improve applications involving noisy inputs or requiring precise inverse mappings, such as image editing and latent space interpolations.

Future work may explore the application of stochastic clipping to other architectures, such as discriminative CNNs, potentially enhancing reconstruction fidelity in these models as well. The generalizability of stochastic clipping to broader domains within neural network inversions also warrants further investigation.

Conclusion

This research provides a compelling solution to the problem of inverting GANs, contributing both methodological advancements and empirical evidence of efficacy. The introduction of stochastic clipping offers a promising avenue for precision reconstruction of latent vectors, with broad implications for machine learning applications where reverse mapping accuracy is critical.