Nvidia Corporation

Miika Samuli Aittala

Samuli Matias Laine

Tero Tapani Karras

Timo Oskari Aila

Jaakko Tapani Lehtinen

David Patrick Luebke

Ming-Yu Liu

Ting-Chun Wang

Arun Mohanray Mallya

Janne Johannes Hellsten

Erik Andreas Härkönen

Onni August Kosomaa

Techniques are disclosed herein for generating a content item. The techniques include receiving a content item and metadata indicating a level of corruption associated with the content item; and for each iteration included in a plurality of iterations: performing one or more operations to add corruption to a first version of the content item to generate a second version of the content item, and performing one or more operations to reduce corruption in the second version of the content item to generate a third version of the content item, wherein a level of corruption associated with the third version of the content item is less than a level of corruption associated with the first version of the content item.

Techniques for content synthesis using denoising diffusion models

Embodiments of the present disclosure relate to a technique for training neural networks, such as a generative adversarial neural network (GAN), using a limited amount of data. Training GANs using too little example data typically leads to discriminator overfitting, causing training to diverge and produce poor results. An adaptive discriminator augmentation mechanism is used that significantly stabilizes training with limited data providing the ability to train high-quality GANs. An augmentation operator is applied to the distribution of inputs to a discriminator used to train a generator, representing a transformation that is invertible to ensure there is no leakage of the augmentations into the images generated by the generator. Reducing the amount of training data that is needed to achieve convergence has the potential to considerably help many applications and may the increase use of generative models in fields such as medicine.

Training neural networks with limited data using invertible augmentation operators

Systems and methods are disclosed for fused processing of a continuous mathematical operator. Fused processing of continuous mathematical operations, such as pointwise non-linear functions without storing intermediate results to memory improves performance when the memory bus bandwidth is limited. In an embodiment, a continuous mathematical operation including at least two of convolution, upsampling, pointwise non-linear function, and downsampling is executed to process input data and generate alias-free output data. In an embodiment, the input data is spatially tiled for processing in parallel such that the intermediate results generated during processing of the input data for each tile may be stored in a shared memory within the processor. Storing the intermediate data in the shared memory improves performance compared with storing the intermediate data to the external memory and loading the intermediate data from the external memory.

Fused processing of a continuous mathematical operator

Systems and methods are disclosed that improve output quality of any neural network, particularly an image generative neural network. In the real world, details of different scale tend to transform hierarchically. For example, moving a person's head causes the nose to move, which in turn moves the skin pores on the nose. Conventional generative neural networks do not synthesize images in a natural hierarchical manner: the coarse features seem to mainly control the presence of finer features, but not the precise positions of the finer features. Instead, much of the fine detail appears to be fixed to pixel coordinates which is a manifestation of aliasing. Aliasing breaks the illusion of a solid and coherent object moving in space. A generative neural network with reduced aliasing provides an architecture that exhibits a more natural transformation hierarchy, where the exact sub-pixel position of each feature is inherited from underlying coarse features.

Generative neural networks with reduced aliasing

A three-dimensional (3D) density volume of an object is constructed from tomography images (e.g., x-ray images) of the object. The tomography images are projection images that capture all structures of an object (e.g., human body) between a beam source and imaging sensor. The beam effectively integrates along a path through the object producing a tomography image at the imaging sensor, where each pixel represents attenuation. A 3D reconstruction pipeline includes a first neural network model, a fixed function backprojection unit, and a second neural network model. Given information for the capture environment, the tomography images are processed by the reconstruction pipeline to produce a reconstructed 3D density volume of the object. In contrast with a set of 2D slices, the entire 3D density volume is reconstructed, so two-dimensional (2D) density images may be produced by slicing through any portion of the 3D density volume at any angle.

Three-dimensional tomography reconstruction pipeline

A latent code defined in an input space is processed by the mapping neural network to produce an intermediate latent code defined in an intermediate latent space. The intermediate latent code may be used as appearance vector that is processed by the synthesis neural network to generate an image. The appearance vector is a compressed encoding of data, such as video frames including a person's face, audio, and other data. Captured images may be converted into appearance vectors at a local device and transmitted to a remote device using much less bandwidth compared with transmitting the captured images. A synthesis neural network at the remote device reconstructs the images for display.

Generative adversarial neural network assisted video reconstruction

Generative adversarial neural network assisted compression and broadcast

A style-based generative network architecture enables scale-specific control of synthesized output data, such as images. During training, the style-based generative neural network (generator neural network) includes a mapping network and a synthesis network. During prediction, the mapping network may be omitted, replicated, or evaluated several times. The synthesis network may be used to generate highly varied, high-quality output data with a wide variety of attributes. For example, when used to generate images of people's faces, the attributes that may vary are age, ethnicity, camera viewpoint, pose, face shape, eyeglasses, colors (eyes, hair, etc.), hair style, lighting, background, etc. Depending on the task, generated output data may include images, audio, video, three-dimensional (3D) objects, text, etc.

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Helsinki, Finland

Miika Samuli Aittala