Abstract: Implementations described herein relate to image texture manipulation for machine learning data augmentation. In some implementations, a computer vision platform may transform first data of a first image file to a first frequency domain representation of the first image file. The computer vision platform may identify, based on the first frequency domain representation, a first subset of frequencies for the first image file. The computer vision platform may transform second data of a second image file to a second frequency domain representation of the second image file. The computer vision platform may identify, based on the second frequency domain representation, a second subset of frequencies for the second image file. The computer vision platform may generate a third image file based on the first subset of frequencies and the second subset of frequencies. The computer vision platform may store the third image file in a set of image files.

Abstract: In some implementations, a memory system may store a dataset in a portion of a fabric-attached memory, wherein the dataset is stored in a format that enables zero-copy analysis of the dataset by multiple host devices associated with a distributed workflow. The memory system may establish a respective direct access connection to the portion of the fabric-attached memory with each host device of the multiple host devices associated with the distributed workflow. The memory system may permit each host device, of the multiple host devices, to access the dataset via the respective direct access connection and by using a zero-copy access technique to extract a batch of data objects from the dataset for performing a computation associated with the distributed workflow.

Abstract: In some implementations, a compute express link (CXL) compliant memory system may configure a portion of a memory as a shared memory region directly accessible by multiple fabric-attached processing units. The CXL compliant memory system may establish, with a first and second fabric-attached processing unit, a first and second device direct access link, respectively, to the shared memory region. The CXL compliant memory system may receive, via the first device direct access link and from the first fabric-attached processing unit, communication information associated with communications between the multiple fabric-attached processing units. The CXL compliant memory system may store the communication information in the shared memory region. The CXL compliant memory system may permit, via the second device direct access link and by using a zero-copy operation, access to the communication information by the second fabric-attached processing unit.

Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

Abstract: A machine learning model may be trained to denoise an image. The machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. The machine learning model may include a recurrent neural network. The machine learning model may have a modular architecture including one or more building units. The machine learning model may have a multi-branch architecture. The noise may be identified and removed from the image by an iterative process.

Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

Abstract: Implementations described herein relate to image texture manipulation for machine learning data augmentation. In some implementations, a computer vision platform may transform first data of a first image file to a first frequency domain representation of the first image file. The computer vision platform may identify, based on the first frequency domain representation, a first subset of frequencies for the first image file. The computer vision platform may transform second data of a second image file to a second frequency domain representation of the second image file. The computer vision platform may identify, based on the second frequency domain representation, a second subset of frequencies for the second image file. The computer vision platform may generate a third image file based on the first subset of frequencies and the second subset of frequencies. The computer vision platform may store the third image file in a set of image files.

Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.