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Found 5 papers, 5 papers with code
Cycle Encoding of a StyleGAN Encoder for Improved Reconstruction and Editability
The recently proposed pivotal tuning model makes significant progress towards reconstruction and editability, by using a two-step approach that first inverts the input image into a latent code, called pivot code, and then alters the generator so that the input image can be accurately mapped into the pivot code.
Adversarial Learning with Mask Reconstruction for Text-Guided Image Inpainting
In this paper, we propose an adversarial learning framework with mask reconstruction (ALMR) for image inpainting with textual guidance, which consists of a two-stage generator and dual discriminators.
Virtual Mixup Training for Unsupervised Domain Adaptation
Existing methods only impose the locally-Lipschitz constraint around the training points while miss the other areas, such as the points in-between training data.
MMED: A Multi-domain and Multi-modality Event Dataset
In this work, we construct and release a multi-domain and multi-modality event dataset (MMED), containing 25, 165 textual news articles collected from hundreds of news media sites (e. g., Yahoo News, Google News, CNN News.)
Learning Shared Semantic Space with Correlation Alignment for Cross-modal Event Retrieval
In this paper, we propose to learn shared semantic space with correlation alignment (${S}^{3}CA$) for multimodal data representations, which aligns nonlinear correlations of multimodal data distributions in deep neural networks designed for heterogeneous data.
