A method of training a generator G of a Generative Adversarial Network (GAN) includes receiving, by an encoder E, a target data Y; receiving, by the encoder E, an output G(Z) of the generator G, where the generator G generates the output G(Z) in response to receiving a random sample Z and where a discriminator D of the GAN is trained to distinguish which of the G(Z) and the target data Y; training the encoder E to minimize a difference between a first latent space representation E(G(Z)) of the output G(Z) and a second latent space representation E(Y) of the target data Y, where the output G(Z) and the target data Y are input to the encoder E; and using the first latent space representation E(G(Z)) and the second latent space representation E(Y) to constrain the training of the generator G.

A method of data preparation for artificial intelligence models includes receiving data characterizing a first plurality of images. The method further includes annotating a first subset of images of the first plurality of images based at least in part on a first user input to generate annotated first subset of images. The annotating includes labelling one or more features of the first subset of images. The method also includes generating, by a training code, an annotation code, the training code configured to receive the annotated first subset of images as input and output the annotation code. The training and the annotation code includes computer executable instructions. The method also includes annotating, by the annotation code, a second subset of images of the first plurality of images to generate annotated second subset of images, wherein the annotating includes labelling one or more features of the second subset of images.

According to some embodiments, disclosed are systems and methods for a novel framework that performs management of a location and the individuals operating therein based on determined fatigue data of such individuals. The framework may track a person (e.g., a user) at or around a location. Such tracking may be performed based on captured digital imagery of the user via a set of strategically positioned cameras at the location. In some embodiments, as soon as a user begins working, or upon detection by a camera(s), the framework may cause the camera(s) to begin capturing footage of the user, which may be fed, uploaded and/or streamed to a fatigue detection system that determines fatigue data related to the user. Such fatigue data may be leveraged to control which jobs certain users are performing, while reassigning other users based on safety decisions formed from their respective fatigue data.

According to some embodiments, disclosed are systems and methods for a novel framework that performs management of a location and the individuals operating therein based on determined fatigue data of such individuals. The framework may track a person (e.g., a user) at or around a location. Such tracking may be performed based on captured digital imagery of the user via a set of strategically positioned cameras at the location. In some embodiments, as soon as a user begins working, or upon detection by a camera(s), the framework may cause the camera(s) to begin capturing footage of the user, which may be fed, uploaded and/or streamed to a fatigue detection system that determines fatigue data related to the user. Such fatigue data may be leveraged to control which jobs certain users are performing, while reassigning other users based on safety decisions formed from their respective fatigue data.

A system and method for machine learning model validation. A method includes: determining a first score distribution for a first run of a machine learning model and a second score distribution for a second run of the machine learning model, wherein the first run includes applying the machine learning model to a first test dataset, wherein the second run includes applying the machine learning model to a second test dataset, wherein the second test dataset is collected after the first test dataset; comparing the first score distribution to the second score distribution; determining, based on the comparison, whether the machine learning model is validated; continuing use of the machine learning model when it is determined that the machine learning model is validated; and performing at least one rehabilitative action with respect to the machine learning model when it is determined that the machine learning model is not validated.

The present invention provides a training method of a neural network model, wherein the training method includes the steps of: receiving image data including a plurality of frames, and for first frames in the frames, the image data further includes detection data, and the detection data includes position of at least one person within the corresponding first frame; and for second frames in the frames, the image data further includes person search data, and the person search data includes position and serial number of at least one person within the corresponding second frame; using the neural network model to perform a person recognition operation on the frames to generate a recognition result; and using loss functions to process the recognition result of each frame, the detection result of each first frame and the person search data of each second frame, for adjusting parameters of the neural network model.

The present invention provides a training method of a neural network model, wherein the training method includes the steps of: receiving image data including a plurality of frames, and for first frames in the frames, the image data further includes detection data, and the detection data includes position of at least one person within the corresponding first frame; and for second frames in the frames, the image data further includes person search data, and the person search data includes position and serial number of at least one person within the corresponding second frame; using the neural network model to perform a person recognition operation on the frames to generate a recognition result; and using loss functions to process the recognition result of each frame, the detection result of each first frame and the person search data of each second frame, for adjusting parameters of the neural network model.

A quality determination method includes: (a) generating a plurality of pieces of training data by classifying a plurality of pieces of non-defective product data into a plurality of classes; (b) executing learning of a machine learning model using the plurality of pieces of training data; (c) preparing a known feature spectrum group; and (d) executing quality determination processing of inspection data using the machine learning model and the known feature spectrum group. The (d) includes (d1) calculating a feature spectrum related to the inspection data, (d2) calculating a similarity between the feature spectrum and the known feature spectrum group, and (d3) determining the inspection data to be non-defective when the similarity is equal to or greater than a threshold value and determining the inspection data to be defective when the similarity is less than the threshold value.

A quality determination method includes: (a) generating a plurality of pieces of training data by classifying a plurality of pieces of non-defective product data into a plurality of classes; (b) executing learning of a machine learning model using the plurality of pieces of training data; (c) preparing a known feature spectrum group; and (d) executing quality determination processing of inspection data using the machine learning model and the known feature spectrum group. The (d) includes (d1) calculating a feature spectrum related to the inspection data, (d2) calculating a similarity between the feature spectrum and the known feature spectrum group, and (d3) determining the inspection data to be non-defective when the similarity is equal to or greater than a threshold value and determining the inspection data to be defective when the similarity is less than the threshold value.

Systems and method for assessing annotators by way of annotated images annotated by said annotators. Agent or annotator model modules are trained using annotated images annotated by specific annotators. A baseline model module is also trained using all of the annotated images used in training the agent model modules. The trained agent model modules are then used to annotate an evaluation dataset to result in evaluation result annotated images. The trained baseline model module is also used to annotate the evaluation dataset to result in its own evaluation result annotated images. The evaluation results from the agent model modules are compared with the evaluation result from the baseline model module. Based on the comparison results, scores are allocated to each agent model module. The scores are used to group agent model modules and annotators that correspond to the low scoring agent model modules can be targeted for retraining.