Static Channel Filtering in Frequency Domain for Machine Learning
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Solution Overview
Problem
Current machine learning systems face computational inefficiencies due to the need to process large, uncompressed image datasets, which incur significant data transfer bottlenecks and computational overhead, especially when training and inference computations are performed on raw image data in the spatial domain without leveraging frequency domain representations for compression and filtering.
Innovation Solution
Implement static channel filtering on image datasets transformed into frequency domain representations, discarding insignificant frequency channels before data transfer and computation, allowing for reduced data volumes and computational resources, and enabling direct input into deeper layers of learning models without inverse transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image datasets are processed in spatial domain without frequency domain transformation, then computational simplicity is maintained, but data transfer volume and computational overhead are excessive
Solution Approach 1:
The patent transforms image data from spatial domain to frequency domain representation, changing the parameter space in which processing occurs. This transformation enables filtering operations that remove insignificant frequency channels, thereby reducing data volume while maintaining essential image information for machine learning computations.
Solution Approach 2:
The patent extracts and removes insignificant frequency channels from the frequency domain representation of image data. By identifying and discarding frequency components that contribute minimally to image quality and machine learning task performance, the system reduces data transfer volume without substantially compromising processing accuracy.
2Quantity of substance
If frequency domain filtering is applied to reduce data volume, then data transfer burden is reduced, but computational complexity of transformation increases
Solution Approach 1:
The patent performs frequency domain transformation and filtering as a preliminary step before the main machine learning computation. By pre-processing the image data to remove insignificant frequency components before transfer and processing, the system reduces the computational burden during the main learning tasks, as the filtering operations are performed once on the dataset rather than repeatedly during training and inference.
3Reliability
If all frequency channels are processed, then complete image information is maintained, but computational resources and time are excessive
Solution Approach 1:
The patent applies partial action by processing only the significant frequency channels rather than all frequency channels. By retaining only those frequency components that substantially contribute to image quality and machine learning task performance, the system achieves adequate image information completeness with reduced computational resources and faster convergence times.
Solution Approach 2:
The patent discards insignificant frequency channels that contribute minimally to image information and machine learning task performance. By removing these redundant components, the system reduces computational overhead and processing time while maintaining the essential image information needed for accurate machine learning computations.
4Reliability
If uncompressed raw images are used for training and inference, then image quality is preserved, but network transport and storage costs are excessive
Solution Approach 1:
The patent changes the representation parameters of image data from spatial domain to frequency domain, enabling more efficient compression. This transformation allows the system to remove redundant frequency information while preserving the essential visual features needed for machine learning tasks, thereby reducing storage and transport volumes without substantially compromising image quality or task performance.
Data Source
AI summary
Methods and systems are provided for implementing static channel filtering operations upon image datasets transformed to frequency domain representations, including decoding images of an image dataset to generate a frequency domain representation of the image dataset; discarding coefficient values of one or more particular frequency channels of each image of the image dataset in a frequency domain representation; and transporting the image dataset in a frequency domain representation to one or more special-purpose processor(s). Methods and systems of the present disclosure may enable a filtered image dataset to be input to a second layer of a learning model, bypassing a first layer, or may enable a learning model to be designed with a reduced-size first layer. This may achieve benefits such as reducing computational overhead and time of machine learning training and inference computations, reducing volume of image data input into the learning model, and reducing convergence time.