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Found 8 papers, 5 papers with code
The Limitations of Cross-language Word Embeddings Evaluation
The aim of this work is to explore the possible limitations of existing methods of cross-language word embeddings evaluation, addressing the lack of correlation between intrinsic and extrinsic cross-language evaluation methods.
Label Denoising with Large Ensembles of Heterogeneous Neural Networks
Despite recent advances in computer vision based on various convolutional architectures, video understanding remains an important challenge.
SEIGAN: Towards Compositional Image Generation by Simultaneously Learning to Segment, Enhance, and Inpaint
We present a novel approach to image manipulation and understanding by simultaneously learning to segment object masks, paste objects to another background image, and remove them from original images.
Learning State Representations in Complex Systems with Multimodal Data
Representation learning becomes especially important for complex systems with multimodal data sources such as cameras or sensors.
DeepLandscape: Adversarial Modeling of Landscape Video
Furthermore, by fitting the learned models to a static landscape image, the latter can be reenacted in a realistic way.
Perceptual Gradient Networks
The use of perceptual loss however incurs repeated forward-backward passes in a large image classification network as well as a considerable memory overhead required to store the activations of this network.
Resolution-robust Large Mask Inpainting with Fourier Convolutions
We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function.
Ranked #3 on
Seeing Beyond the Visible
on KITTI360-EX
DeepLandscape: Adversarial Modeling of Landscape Videos
Furthermore, by fitting the learned models to a static landscape image, the latter can be reenacted in a realistic way.
