arXiv Machine Learning
Aug 11

From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations in Image Recognition

arXiv:2308. 04553v4 Announce Type: replace-cross Abstract: Visual recognition models are prone to learning spurious correlations induced by a biased training set where certain conditions $B$ (\eg, Indoors) are over-represented in certain classes $Y$ (\eg, Big Dogs).

By Maan Qraitem, Kate Saenko, Bryan A. Plummer
arXiv Machine Learning
Sep 22

EmbeddGAN: A Novel GAN Framework Using an Embedding Network and Gini Distance Correlation

EmbeddGAN introduces a new GAN framework that replaces the traditional discriminator with an embedding network trained to maximize statistical dependence between embeddings and real/fake labels using Gini distance correlation (gCor). The generator simultaneously minimizes this dependence, encouraging real and generated samples to become indistinguishable in the learned low‑dimensional embedding space. Experiments on MNIST, CIFAR‑10, and CelebA show competitive performance and notably more stable training dynamics compared to established baselines.

By MaTais Caldwell, Yixin Chen, Xin Dang, Charles Walter