Apparatus and method for training a machine learning model to augment signal data and image data
An apparatus and method for training a machine learning model to augment signal data and image data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a signal data. The memory instructs the processor to generate a digital image, wherein the digital image comprises the signal data. The memory instructs the processor to transmit the digital image to an image processing module, wherein the image processing module produces an augmented image. The memory instructs the processor to transmit the signal data to a signal processing module, wherein the signal processing module produces the augmented image. The memory instructs the processor to train a machine learning model using the augmented image.
1 . An apparatus for training a machine learning model to augment signal data and image data, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
receive signal data;
generate a digital image, wherein the digital image comprises the signal data;
transmit the digital image to an image processing module, wherein the image processing module produces a first augmented image;
transmit the signal data to a signal processing module, wherein the signal processing module produces a second augmented image; and
train a machine learning model using the first augmented image and the second augmented image as inputs and a computer generated image of the signal data as outputs.
2 . The apparatus of claim 1 , wherein generating the digital image comprises generating the digital image by processing the signal data and wherein processing the signal data comprises:
mapping the signal data to pixel values of the digital image; and
plotting the signal data to an image canvas.
3 . The apparatus of claim 1 , wherein transforming the digital image into the first augmented image comprises transforming the digital image as a function of a generative representation model, wherein the generative representation model comprises a plurality of image processing modules.
4 . The apparatus of claim 1 , wherein the image processing module is configured to:
receive the digital image from a digital image repository; and
modify the digital image.
5 . The apparatus of claim 1 , wherein transforming the signal data into the augmented image comprises transforming the signal data as a function of a generative representation model, wherein the generative representation model comprises a plurality of signal processing modules.
6 . The apparatus of claim 1 , wherein the signal processing module is configured to:
receive the signal data from a signal data repository; and
modify the signal data.
7 . The apparatus of claim 1 , wherein the first augmented image and the second augmented image are used to train a signal extraction module and a downstream task model.
8 . The apparatus of claim 7 , wherein the signal extraction module extracts digital signals from the digital image.
9 . The apparatus of claim 1 , wherein the machine learning model comprises a neural network, wherein the neural network is configured to:
receive a database comprising extensible markup language formatted digital images;
classify the database of extensible markup language formatted digital images; and
generate the outputs.
10 . The apparatus of claim 9 , wherein the neural network comprises a universal or multi layout based neural network model.
11 . A method for training a machine learning model to augment signal data and image data, the method comprising:
receiving signal data;
generating a digital image, wherein the digital image comprises the signal data;
transmitting the digital image to an image processing module, wherein the image processing module produces a first augmented image;
transmitting the signal data to a signal processing module, wherein the signal processing module produces a second augmented image; and
training a machine learning model using the first augmented image and the second augmented image as inputs and a computer generated image of the signal data as outputs.
12 . The method of claim 11 , wherein generating the digital image comprises generating the digital image by processing the signal data and wherein processing the signal data comprises:
mapping the signal data to pixel values of the digital image; and
plotting the signal data to an image canvas.
13 . The method of claim 11 , wherein transforming the digital image into the first augmented image comprises transforming the digital image as a function of a generative representation model, wherein the generative representation model comprises a plurality of image processing modules.
14 . The method of claim 11 , wherein the image processing module is configured to:
receive the digital image from a digital image repository; and
modify the digital image.
15 . The method of claim 11 , wherein transforming the signal data into the augmented image comprises transforming the signal data as a function of a generative representation model, wherein the generative representation model comprises a plurality of signal processing modules.
16 . The method of claim 11 , wherein the signal processing module is configured to:
receive the signal data from a signal data repository; and
modify the signal data.
17 . The method of claim 11 , wherein the first augmented image and the second augmented image are used to train a signal extraction module and a downstream task model.
18 . The method of claim 17 , wherein the signal extraction module extracts digital signals from the digital image.
19 . The method of claim 11 , wherein the machine learning model comprises a neural network, wherein the neural network is configured to:
receive a database comprising extensible markup language formatted digital images;
classify the database of extensible markup language formatted digital images; and
generate the outputs.
20 . The method of claim 19 , wherein the neural network comprises a universal or multi layout based neural network model.
21 . The apparatus of claim 1 , wherein the at least a processor is further configured to feed the outputs of the trained machine learning model to a downstream task model.
22 . The method of claim 11 , the method further comprising feeding the outputs of the trained machine learning model to a downstream task model.