Disentangled representation learning
Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …
and disentangling the underlying factors hidden in the observable data in representation …
Automated disentangled sequential recommendation with large language models
Sequential recommendation aims to recommend the next items that a target user may have
interest in based on the user's sequence of past behaviors, which has become a hot …
interest in based on the user's sequence of past behaviors, which has become a hot …
Diff-privacy: Diffusion-based face privacy protection
Privacy protection has become a top priority due to the widespread collection and misuse of
personal data. Anonymization and visual identity information hiding are two crucial tasks in …
personal data. Anonymization and visual identity information hiding are two crucial tasks in …
Catversion: Concatenating embeddings for diffusion-based text-to-image personalization
We propose CatVersion, an inversion-based method that learns the personalized concept
through a handful of examples. Subsequently, users can utilize text prompts to generate …
through a handful of examples. Subsequently, users can utilize text prompts to generate …
Scenedreamer360: Text-driven 3d-consistent scene generation with panoramic gaussian splatting
Text-driven 3D scene generation has seen significant advancements recently. However,
most existing methods generate single-view images using generative models and then stitch …
most existing methods generate single-view images using generative models and then stitch …
Disentangled Dynamic Graph Attention Network for Out-of-Distribution Sequential Recommendation
Sequential recommendation, leveraging user-item interaction histories to provide
personalized and timely suggestions, has drawn significant research interest recently. With …
personalized and timely suggestions, has drawn significant research interest recently. With …
Disentangled representation learning with transmitted information bottleneck
Encoding only the task-related information from the raw data, ie, disentangled
representation learning, can greatly contribute to the robustness and generalizability of …
representation learning, can greatly contribute to the robustness and generalizability of …
Prior Preserved Text-to-Image Personalization without Image Regularization
Z Wang, O Li, T Wang, L Wei, Y Hao… - … on Circuits and …, 2024 - ieeexplore.ieee.org
The current state-of-the-art text-to-image (T2I) models have found numerous applications,
driven by their ability to produce photorealistic images. Concept learning, as one notable …
driven by their ability to produce photorealistic images. Concept learning, as one notable …
CoDi: Contrastive Disentanglement Generative Adversarial Networks for Zero-Shot Sketch-Based 3D Shape Retrieval
M Meng, W Chen, J Liu, J Yu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Sketch-based 3D shape retrieval has attracted increasing attention in recent years. Most
existing methods fail to address the zero-shot scenario, and the few dedicated to zero-shot …
existing methods fail to address the zero-shot scenario, and the few dedicated to zero-shot …
Fast Sampling of Diffusion Models for Accelerated MRI using Dual Manifold Constraints
Diffusion models show great potential in solving inverse problems, including MRI
reconstruction. With its unique characteristics, medical imaging demands both efficiency and …
reconstruction. With its unique characteristics, medical imaging demands both efficiency and …