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Found 12 papers, 2 papers with code
Fast and Stable Diffusion Inverse Solver with History Gradient Update
To address this issue, we propose to utilize the history information of the diffusion-based inverse solvers.
Hierarchical Disentangled Representation for Invertible Image Denoising and Beyond
More specifically, we decompose the noisy image into clean low-frequency and hybrid high-frequency parts with an invertible transformation and then disentangle case-specific noise and high-frequency components in the latent space.
Depth Completion using Geometry-Aware Embedding
The ablation study gives more insights into our method that could achieve significant gains with a simple design, while having better generalization capability and stability.
Avoiding Overlap in Data Augmentation for AMR-to-Text Generation
We propose methods for excluding parts of Gigaword to remove this overlap, and show that our approach leads to a more realistic evaluation of the task of AMR-to-text generation.
Learning Invariant Representation for Unsupervised Image Restoration
Recently, cross domain transfer has been applied for unsupervised image restoration tasks.
Learning to Order Graph Elements with Application to Multilingual Surface Realization
Recent advances in deep learning have shown promises in solving complex combinatorial optimization problems, such as sorting variable-sized sequences.
Boosting Dialog Response Generation
Neural models have become one of the most important approaches to dialog response generation.
Top-Down Structurally-Constrained Neural Response Generation with Lexicalized Probabilistic Context-Free Grammar
We consider neural language generation under a novel problem setting: generating the words of a sentence according to the order of their first appearance in its lexicalized PCFG parse tree, in a depth-first, left-to-right manner.
Visual Attention Network for Low Dose CT
Noise and artifacts are intrinsic to low dose CT (LDCT) data acquisition, and will significantly affect the imaging performance.
Data Augmentation for Neural Online Chats Response Selection
Data augmentation seeks to manipulate the available data for training to improve the generalization ability of models.
Data Augmentation for Neural Online Chat Response Selection
Data augmentation seeks to manipulate the available data for training to improve the generalization ability of models.
Discovering Conversational Dependencies between Messages in Dialogs
We investigate the task of inferring conversational dependencies between messages in one-on-one online chat, which has become one of the most popular forms of customer service.
