Disentanglement
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Benchmarks
Most implemented
A Style-Based Generator Architecture for Generative Adversarial Networks
Disentangling by Factorising
Adversarial Latent Autoencoders
Sigmoid Loss for Language Image Pre-Training
Isolating Sources of Disentanglement in Variational Autoencoders
Papers
CSD-VAR: Content-Style Decomposition in Visual Autoregressive Models
Disentangling content and style from a single image, known as content-style decomposition (CSD), enables recontextualization of extracted content and stylization of extracted styles, offering greater creative flexibility…
DisentanglementTowards Imperceptible JPEG Image Hiding: Multi-range Representations-driven Adversarial Stego Generation
Deep hiding has been exploring the hiding capability of deep learning-based models, aiming to conceal image-level messages into cover images and reveal them from generated stego images. Existing schemes are easily detect…
DisentanglementSteganalysisGenerative Head-Mounted Camera Captures for Photorealistic Avatars
Enabling photorealistic avatar animations in virtual and augmented reality (VR/AR) has been challenging because of the difficulty of obtaining ground truth state of faces. It is physically impossible to obtain synchroniz…
DisentanglementReflections Unlock: Geometry-Aware Reflection Disentanglement in 3D Gaussian Splatting for Photorealistic Scenes Rendering
Accurately rendering scenes with reflective surfaces remains a significant challenge in novel view synthesis, as existing methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) often misinterpret ref…
3DGSDisentanglementNeRFNovel View Synthesis+1Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations
Domain Generalization (DG) aims to enhance model robustness in unseen or distributionally shifted target domains through training exclusively on source domains. Although existing DG techniques, such as data manipulation,…
DisentanglementDomain GeneralizationCausal-SAM-LLM: Large Language Models as Causal Reasoners for Robust Medical Segmentation
The clinical utility of deep learning models for medical image segmentation is severely constrained by their inability to generalize to unseen domains. This failure is often rooted in the models learning spurious correla…
AnatomyDisentanglementImage SegmentationMedical Image Segmentation+1