Abstract: Systems and methods for automatically enhancing the quality of images in the training set of a neural network NN. A method includes gaining access to a training set including a plurality of images. Using at least one image quality assessment method, at least one image is identified from a plurality of images in the training set, which matches a low-quality criterion as at least one low-quality image. At least one image enhancement method is used for enhancing the at least one low-quality image to obtain at least one enhanced image. The at least one low-quality image is replaced with the corresponding at least one enhanced image in the training set.

Abstract: Systems and methods for automatically identifying outliers in Machine Learning training datasets. The method includes gaining access to a training set for the neural network NN. For each element of the training dataset, an embedding vector is generated, which is a numeric representation of the corresponding element. A centroid of all the embedding vectors of all the elements of the training set is computed equal to an average of all the embedding vectors of all the elements of the training set. A dissimilarity score is generated for each element of the training set by calculating a distance between the embedding vector corresponding to the element and the centroid. The method further includes identifying the elements from the training set with embedding vectors having the dissimilarity score higher than or equal to a predetermined threshold value.

Abstract: Systems and methods for automatically enhancing the quality of images in the training set of a neural network NN. A method includes gaining access to a training set including a plurality of images. Using at least one image quality assessment method, at least one image is identified from a plurality of images in the training set, which matches a low-quality criterion as at least one low-quality image. At least one image enhancement method is used for enhancing the at least one low-quality image to obtain at least one enhanced image. The at least one low-quality image is replaced with the corresponding at least one enhanced image in the training set.

Abstract: Systems and methods for augmenting a training dataset. The method includes gaining access to at least one insufficient training dataset for training a neural network NN. A generative convolutional neural network GCNN is trained using the training set or a subset thereof. At least one additional item is generated by the GCNN trained on the existing training set, and the generated item is added to the original training set.