The paper investigates treating LoRA adapters as a data modality by training a transformer autoencoder to map adapter weights into a learned latent space. It finds that this latent representation preserves the same capabilities as raw weight space—such as sampling, editing, and inversion—while offering greater robustness to displacement, producing distorted yet valid faces instead of texture loss. Additionally, the authors extend the model to accommodate mixed-rank adapters, enabling a single shared latent to host heterogeneous adapter architectures.
By Moritz Gr\"avinghoff, Konstantin Wehmeyer, Aron Asefaw, Boris Knyazev, Damian Borth
arXiv:2410. 10137v5 Announce Type: replace Abstract: We develop Riemannian approaches to variational autoencoders (VAEs) for PDE-type ambient data with regularizing geometric latent dynamics, which we refer to as VAE-DLM, or VAEs with dynamical latent manifolds.
By Andrew Gracyk
arXiv:2501. 09876v3 Announce Type: replace-cross Abstract: Generative modeling aims to generate new data samples that resemble a given dataset.
By Wonjun Lee, Riley C. W. O'Neill, Dongmian Zou, Jeff Calder, Gilad Lerman
arXiv:2505. 04486v4 Announce Type: replace-cross Abstract: Flow matching models have shown great potential in image generation tasks among probabilistic generative models.
By Anirban Samaddar, Yixuan Sun, Viktor Nilsson, Sandeep Madireddy
arXiv:2608. 08135v1 Announce Type: cross Abstract: Cross-modality medical image translation can reduce the burden of multi-modal acquisitions, yet the field remains constrained by two coupled limitations: methods operate on 2D slices or 3D patches rather than whole volumes, and train a separate model for each translation task.
By Daniele Molino, Alessio Zoboli, Camillo Maria Caruso, Valerio Guarrasi, Paolo Soda
arXiv:2607. 03551v1 Announce Type: new Abstract: Weight space learning aims to learn representations of neural network (NN) weights, enabling different downstream tasks.
By Aron Asefaw, Konstantinos Tzevelekakis, Damian Falk, L\'eo Meynent, Damian Borth