Abstract: Provided are a method for evaluating performance and a system thereof. The method according to some embodiments may include obtaining a first model trained using a labeled dataset, obtaining a second model built by performing unsupervised domain adaptation on the first model, generating pseudo labels for an evaluation dataset using the second model, wherein the evaluation dataset is an unlabeled dataset, and evaluating performance of the first model using the pseudo labels.

Abstract: Training of a machine vision model, a segmentation model, is performed by using an acquisition function for a small number of pixels of one or more training images. The acquisition function uses first mutual information and second mutual information to identify unlabelled pixels which are labelled with high uncertainty when predicting possible label values. Training, prediction of labels, identifying pixels with highly uncertain labels, obtaining labels only for those pixels with highly uncertain labels and retraining are performed iteratively to finally provide the machine vision model. The iterative approach uses very few labelled pixels to obtain the final machine vision model. The machine vision model accurately labels areas of a data image.

Abstract: An active learning classifier engine is provided to reduce consumption of computer resources in acquiring data points for training of a model and then classifying data. The active learning classifier engine uses an acquisition function under a Bayesian active learning framework for acquiring the data points (“BABA”) from unlabeled training data. The acquisition function captures mutual information between the model parameters and the predictive outputs of the unlabeled training data and acquires useful unlabeled training data points which reduce classification errors of the model when classifying previously-unseen data by more properly and quickly placing decision boundaries used in the classification of the previously-unseen data.

Abstract: A problem of supervised learning is overcome by using patches to discover objects in unlabeled training images. The discovered objects are embedded in a pattern space. An AI machine replaces manual entry steps of training with a machine-centric process including clustering in a pixel space, clustering in latent space and building the pattern space based on different losses derived from pixel space clustering and latent space clustering. A distance structure in the pattern space captures the co-occurrence of patterns due to frequently appearing objects in training image data. Embodiments provide image representation based on local image patch naturally handles the position and scale invariance property that is important to effective object detection. Embodiments successfully identifies frequent objects such as human faces, human bodies, animals, or vehicles from unorganized data images based on a small quantity of training images.

Abstract: A problem of imbalanced big data is solved by decoupling a classifier into a neural network for generation of representation vectors and into a classification model for operating on the representation vectors. The neural network and the classification model act as a mapper classifier. The neural network is trained with an unsupervised algorithm and the classification model is trained with a supervised active learning loop. An acquisition function is used in the supervised active learning loop to speed arrival at an accurate classification performance, improving data efficiency. The accuracy of the hybrid classifier is similar to or exceeds the accuracy of comparative classifiers in all aspects. In some embodiments, big data includes an imbalance of more than 10:1 in image classes. The hybrid classifier reduces labor and improves efficiency needed to arrive at an accurate classification performance, and improves recognition of previously-unrecognized images.