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Found 11 papers, 4 papers with code
UltrAvatar: A Realistic Animatable 3D Avatar Diffusion Model with Authenticity Guided Textures
Recent advances in 3D avatar generation have gained significant attentions.
Factorized Tensor Networks for Multi-Task and Multi-Domain Learning
In this paper, we propose a factorized tensor network (FTN) that can achieve accuracy comparable to independent single-task/domain networks with a small number of additional parameters.
Compressive Sensing with Tensorized Autoencoder
Furthermore, in many cases, parts of images can be corrupted by noise or missing entries.
Incremental Task Learning with Incremental Rank Updates
Our method also offers better memory efficiency compared to episodic memory- and mask-based approaches.
Continual Learning via Low-Rank Network Updates
To update the network for a new task, we learn a low-rank (or rank-1) matrix and add that to the weights of every layer.
Provably Convergent Algorithms for Solving Inverse Problems Using Generative Models
The traditional approach of hand-crafting priors (such as sparsity) for solving inverse problems is slowly being replaced by the use of richer learned priors (such as those modeled by deep generative networks).
Solving Phase Retrieval with a Learned Reference
We performed a number of simulations on a variety of datasets under different conditions and found that our proposed method for phase retrieval via unrolled network and learned reference provides near-perfect recovery at fixed (small) computational cost.
Non-Adversarial Video Synthesis with Learned Priors
Different from these methods, we focus on the problem of generating videos from latent noise vectors, without any reference input frames.
Generative Models for Low-Dimensional Video Representation and Compressive Sensing
In the context of compressive sensing, if the unknown image belongs to the range of a pretrained generative network, then we can recover the image by estimating the underlying compact latent code from the available measurements.
Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval
We empirically show that the performance of our method with projected gradient descent is superior to the existing approach for solving phase retrieval under generative priors.
Generative Models for Low-Rank Video Representation and Reconstruction
Finding compact representation of videos is an essential component in almost every problem related to video processing or understanding.
