Abstract: In one respect, there is provided a system that may include a processor and a memory. The memory may be configured to store instructions that results in operations when executed by the processor. The operations may include: training a machine learning model by at least processing a training set with the machine learning model, the training set including at least one synthetic image that is generated by applying one or more modifications to a non-synthetic image; determining, based at least on a result of the processing of the mixed training set, that the machine learning model is unable to classify images having a specific modification; and training the machine learning model with additional training data that includes one or more additional synthetic images having the specific modification. Related methods and articles of manufacture are also disclosed.

Abstract: In one respect, there is provided a system that may include a processor and a memory. The memory may be configured to store instructions that results in operations when executed by the processor. The operations may include: processing an image set with a convolutional neural network configured to detect, in the image set, a first feature and a second feature; determining a respective effectiveness of the first feature and the second feature in enabling the convolutional neural network to classify images in the image set; determining, based at least on the respective effectiveness of the first feature and the second feature, a first initial weight for the first feature and a second initial weight for the second feature; and initializing the convolutional neural network for training, the initialization of the convolutional neural network comprising configuring the convolutional neural network to apply the first initial weight and the second initial weight.

Abstract: In one respect, there is provided a system that may include a processor and a memory. The memory may be configured to store instructions that results in operations when executed by the processor. The operations may include: generating a concatenated feature map set by at least combining a first feature map set and a second feature map set, the first feature map set and the second feature map set each indicating one or more occurrences of a feature within an image, the first feature map set having a different scale than the second feature map set; and classifying, by a convolutional neural network, an image, the classifying of the image being based at least on the concatenated feature map set. Related methods and articles of manufacture are also disclosed.

Abstract: In one respect, there is provided a system that may include a processor and a memory. The memory may be configured to store instructions that results in operations when executed by the processor. The operations may include: generating a concatenated feature map set by at least combining a first feature map set and a second feature map set, the first feature map set and the second feature map set each indicating one or more occurrences of a feature within an image, the first feature map set having a different scale than the second feature map set; and classifying, by a convolutional neural network, an image, the classifying of the image being based at least on the concatenated feature map set. Related methods and articles of manufacture are also disclosed.

Abstract: In one respect, there is provided a system that may include a processor and a memory. The memory may be configured to store instructions that results in operations when executed by the processor. The operations may include: training a machine learning model by at least processing a training set with the machine learning model, the training set including at least one synthetic image that is generated by applying one or more modifications to a non-synthetic image; determining, based at least on a result of the processing of the mixed training set, that the machine learning model is unable to classify images having a specific modification; and training the machine learning model with additional training data that includes one or more additional synthetic images having the specific modification.. Related methods and articles of manufacture are also disclosed.

Abstract: In one respect, there is provided a system that may include a processor and a memory. The memory may be configured to store instructions that results in operations when executed by the processor. The operations may include: processing an image set with a convolutional neural network configured to detect, in the image set, a first feature and a second feature; determining a respective effectiveness of the first feature and the second feature in enabling the convolutional neural network to classify images in the image set; determining, based at least on the respective effectiveness of the first feature and the second feature, a first initial weight for the first feature and a second initial weight for the second feature; and initializing the convolutional neural network for training, the initialization of the convolutional neural network comprising configuring the convolutional neural network to apply the first initial weight and the second initial weight.

Abstract: A hierarchical collection of items including one or more sub collections of items ordered in a hierarchy is received. A statistical measure of frequency of an item in a sub collection of items is determined. Further, statistical measures of weightages of the item are determined defining a number of sub collections in the hierarchical collection of items in which the item appears and a number of sub collections in which the item appears. A statistical measure of variability defining a number of occurrences of the item in the hierarchical collection of items across different sub collections is calculated. Furthermore, a relevance score of the item is determined based on the statistical measure of frequency, the one or more statistical measures of weightages of the item and the statistical measure of variability. Based on the relevance score, the item is presented on a computer generated graphical user interface.