Intel Corporation

Bhavin V Nayak

Gilad Michael

Furkan Isikdogan

Joao Peralta Moreira

Chyuan-Tyng Wu

Avi Kalderon

Sushma Rao

Pavel Kounitsky

Ben Berlin

Masayoshi Asama

An apparatus to facilitate partially-frozen neural networks for efficient computer vision systems is disclosed. The apparatus includes a frozen core to store fixed weights of a machine learning model, one or more trainable cores coupled to the frozen core, the one or more trainable cores comprising multipliers for trainable weights of the machine learning model, and wherein the alpha blending layer includes a trainable alpha blending parameter, and wherein the trainable alpha blending parameter is a function of a trainable parameter, a sigmoid function, and outputs of frozen and trainable blocks in a preceding layer of the machine learning model.

Partially-frozen neural networks for efficient computer vision systems

An apparatus, method, and a computer readable medium for attenuating visual artifacts in processed images. An annotated dataset of images to be processed by an image processing system is created. An adversarial control network is trained to operate as an image quality expert in classifying images. After the adversarial control network has been trained, the adversarial control network is used to supervise the image processing system on-the-fly.

Attenuating visual artifacts of image processing systems using adversarial networks on-the-fly

An example apparatus for processing images includes a hybrid infinite impulse response-finite impulse response (IIR-FIR) convolution block to receive an image and generate processed image information. The hybrid IIR-FIR convolution block includes a vertical infinite impulse response (IIR) component to approximate a vertical convolution when processing the image.

Processing images using hybrid infinite impulse response (TTR) and finite impulse response (FIR) convolution block

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San Jose, CA, United States of America

Bhavin V Nayak