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Found 6 papers, 2 papers with code
On the De-duplication of LAION-2B
Generative models, such as DALL-E, Midjourney, and Stable Diffusion, have societal implications that extend beyond the field of computer science.
Training face verification models from generated face identity data
We find that the addition of a small amount of private data greatly improves the performance of our model, which highlights the limitations of using synthetic data to train machine learning models.
This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces
Recently, generative adversarial networks (GANs) have achieved stunning realism, fooling even human observers.
On the Theoretical Equivalence of Several Trade-Off Curves Assessing Statistical Proximity
The recent advent of powerful generative models has triggered the renewed development of quantitative measures to assess the proximity of two probability distributions.
Detecting Overfitting of Deep Generative Networks via Latent Recovery
Using this methodology, this paper shows that overfitting is not detectable in the pure GAN models proposed in the literature, in contrast with those using hybrid adversarial losses, which are amongst the most widely applied generative methods.
Deep multi-scale architectures for monocular depth estimation
This paper aims at understanding the role of multi-scale information in the estimation of depth from monocular images.
