MAVias: Mitigate any Visual Bias

The study benchmarks various deep learning-based object detection models using the MobilTelesco dataset, which features sparse astrophotography images. Traditional datasets like ImageNet and COCO focus on everyday objects, lacking the unique challenges presented by feature-deficient conditions. The research highlights the difficulties these models face when applied to non-commercial domains, emphasizing the need for specialized datasets in astrophotography.

The article discusses a novel approach to enhancing the diversity of outputs in Fractal Generative Models (FGMs) while maintaining high visual quality. By incorporating the Hausdorff Dimension (HD), a concept from fractal geometry that quantifies structural complexity, the authors propose a learnable HD estimation method that predicts HD from image embeddings. This method aims to improve the diversity of generated images, addressing challenges such as image quality degradation and limited diversity enhancement in FGMs.

Standard Latent Diffusion Models utilize a complex architecture comprising separate encoder, decoder, and diffusion network components, which are trained in multiple stages. This modular design is computationally inefficient and leads to suboptimal performance. The proposed solution aims to unify these components into a single, end-to-end trainable network. The authors identify issues of instability in naive joint training due to 'latent collapse' and introduce Diffusion as Self-Distillation (DSD), a framework that addresses these challenges.

MeanFlow (MF) is a generative model inspired by diffusion processes, designed for efficient few-step generation by learning direct transitions from noise to data. It is commonly utilized as a latent MF, employing the pre-trained Stable Diffusion variational autoencoder (SD-VAE) for high-dimensional data modeling. However, MF training is computationally intensive and often unstable. This study introduces an efficient training and sampling scheme for MF in the latent space of a Representation Autoencoder (RAE), addressing issues like gradient explosion during training.