GenerativeImageModelingUsingSpatialLSTMs LucasTheis UniversityofT¨ubingen 72076T¨ubingen,Germany lucas@bethgelab MatthiasBethge UniversityofT¨ubingen 72076T¨ubingen,Germany matthias@bethgelab Abstract Modelingthedistributionofnaturalimagesischallenging,partlybecauseof strongstatisticaldependencieswhichcanextendove...
We here introduce a recurrent image model based on multidimensional long short-term memory units which are particularly suited for image modeling due to their spatial structure. Our model scales to images of arbitrary size and its likelihood is computationally tractable. We find that it outperforms ...
[86] used spatial LSTM (sLSTM), a multi-dimensional LSTM which is suitable for image modeling because of its spatial structure. However, an immense amount of time is needed to train the LSTM layers considering the number of pixels in the larger datasets such as CIFAR-10 [87] and Image...
36 Generative Image Modeling using Style and Structure Adversarial Networks (S^2GAN) [pdf] 2016 82 37 BEGAN: Boundary Equilibrium Generative Adversarial Networks [pdf] 2017 82 38 Learning from Simulated and Unsupervised Images through Adversarial Training (SimGAN) by Apple [pdf] 2016 80 39 Unrolle...
Long short-term memory37 and CNN–LSTM were evaluated in combination with the above three decoders. Long short-term memory is used in character-level text modeling. The embedding space from the multicategorical and autoregressive models was still inadequate using either encoder (Supplementary Section...
VideoGen: Generative modeling of videos using VQ-VAE and transformers. Anonymous https://openreview.net/forum?id=3InxcRQsYLf Goal-conditioned variational autoencoder trajectory primatives with continuous and discrete latent codes. Osa, Ikemoto https://link.springer.com/article/10.1007/s42979-020-00324...
image change detection tasks, which regards the detection model as a generator and attains the optimal weights of the detection model without increasing the parameters of the detection model through generative-adversarial strategy, boosting the spatial contiguity of predictions. Moreover, We design a ...
We do so by using a pyramid of 3D and 2D convolutional networks to model temporal information while reducing model param- eters and training time, along with an image and a video discriminator. SinGAN-GIF can generate similar looking video samples for natural...
5.2.2 Assessment of image quality human annotators and Inception score (IS) Amazon Mechanical Turk (MTurk), using the web interface, is used, in whichannotators are asked to distinguish between generated data and real data. However, human annotation is expensive, especially for large dataset. ...
In the summer of 2015, we were entertained by Google’s DeepDream algorithm turning an image into a psychedelic mess of dog eyes and pareidolic artifacts; in 2016, we started using smartphone applications to turn photos into paintings of various styles. In the summer of 2016, an experimental ...