arXiv:2607. 17264v1 Announce Type: new Abstract: Disentangled representation learning is a powerful paradigm for robust attribute prediction.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2606. 05109v1 Announce Type: new Abstract: To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interactions without sacrificing modality-specific information.
To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interactions without sacrificing modality-specific information. Learning disentangled representations is a principled way to identify these underlying shared and unique factors that are hidden in observational data.
arXiv:2410. 11251v2 Announce Type: replace Abstract: A hallmark of intelligent agents is the ability to learn reusable skills purely from unsupervised interaction with the environment.
arXiv:2503. 09679v2 Announce Type: replace Abstract: Meta-learning represents a strong class of approaches for solving few-shot learning tasks.
arXiv:2601. 21688v2 Announce Type: replace-cross Abstract: Disentangled representation learning aims to map independent factors of variation to independent representation components.
The paper introduces DiGA, a Disentangled Geometry-Aware framework for merging pretrained models. DiGA orthogonally decomposes each task vector into components tied to distinct geometric attributes, aggregates these components independently, and then recombines them, thereby preserving each component’s geometric identity. Experiments across various models, tasks, and merging methods show that DiGA improves merged-model performance and reduces capability degradation.