A methodology for auditing the visual diversity of unlabeled human face image datasets uses a set of core human interpretable dimensions derived from human similarity judgments. Given a face image, a model can output dimensional values aligned with the human mental representational space of faces, where values not only express the presence of a feature, but also its extent. Since the model can be learned entirely from human behavior, the learned dimensions are not biased toward features that are easier to verbalize or quantify.

Systems, methods, and computer program products for performing semi-supervised contrastive learning of visual representations are provided. For example, the present disclosure provides systems and methods that leverage particular data augmentation schemes and a learnable nonlinear transformation between the representation and the contrastive loss to provide improved visual representations. Further, the present disclosure also provides improvements for semi-supervised contrastive learning. For example, computer-implemented method may include performing semi-supervised contrastive learning based on a set of one or more unlabeled training data, generating an image classification model based on a portion of a plurality of layers in a projection head neural network used in performing the contrastive learning, performing fine-tuning of the image classification model based on a set of one or more labeled training data, and after performing the fine-tuning, distilling the image classification model to a student model comprising a relatively smaller number of parameters than the image classification model.

Solutions for visual search and discovery include performing unsupervised training of a generative adversarial network that has a generator and an assessor. Training the generative adversarial network involves alternating training the assessor with the generator and a plurality of catalog images with training the generator with the assessor. The catalog images are inverted into catalog vectors by leveraging the trained generator. A query image is inverted into a query vector, and image similarity is determined by calculating a distance between the query vector and a catalog vector. In some examples, inversion is performed by training an encoder with the trained generator and inverting the catalog images with the encoder. In some examples, the trained generator is used to perform a search in a vector space. A weighting vector may be used to weight elements of the vectors, effectively prioritizing image features for image similarity determination.

A method of augmenting the number of labeled images for training a neural network comprising the steps of—Starting from a dataset of labeled images with corresponding segmentation masks and a dataset of unlabeled images, gathering for a given image i in a data set of labeled images a number of images with similar metadata in said dataset of unlabeled images so as to form data sub-set Sim i,—Training a multiclass segmentation neural network on said labeled images thereby generating segmentation masks for the images in subset Sim i,—On the basis of these segmentation masks judging similarity between images of Sim i and image i and finding the most similar image(s) in Sim i by computing and comparing histograms of segmentation masks of image i and images in Sim i—Transferring the histogram of the most similar images in Sim i to given image i.

A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of video features in the digital video data, analyzing the plurality of video features to create a relationship graph, determining a relationship score based on the relationship graph using a first computer-implemented machine learning model, and outputting the relationship score with a user interface. The interacting event comprises a plurality of interactions between a first individual and a second individual and each video feature of the plurality of video features corresponds to an interaction of the plurality of interactions. The relationship graph comprises a first node, a second node, and a first edge extending from the first node to the second node. The first node represents the first individual, the second node represents the second individual, and a weight of the first edge represents a relationship strength between the first individual and the second individual.

A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of features in the digital video data, and analyzing the plurality of features to create a relationship graph. The relationship graph comprises a plurality of nodes and a plurality of edges, each node of the plurality of nodes represents an individual of the plurality of individuals, and each edge of the plurality of edges extends between two nodes of the plurality of nodes, and the plurality of edges represents a plurality of interactions of the interacting event. The method further includes identifying an edge of the plurality of edges as an anomalous edge, creating an output representative of the anomalous edge, and outputting the output representative of the anomalous edge. The anomalous edge is identified by a computer-implemented machine learning model configured to identify anomalous edges in relationship graphs.

A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of features in the digital video data with a first computer-implemented machine learning model, analyzing the plurality of features to create a baseline relationship graph, determining a target relationship graph, generating one or more actions for increasing similarity between the baseline relationship graph and the target relationship graph, and outputting the one or more actions by a user interface. The one or more actions are generated using a simulator, a second computer-implemented machine learning model, and a plurality of actions. The second computer-implemented machine learning model is configured to relate actions of the plurality of actions to changes to relationship graphs, the simulator is configured to simulate changes to the baseline relationship graph using the second computer-implemented machine learning model and the plurality of actions.

Provided herein are methods for automated image quality control (QC). The method comprises: generating training data based at least in part on metadata obtained from a data augmentation process; training a model for a QC task based at least in part on the training data. The model is trained using a self-supervised learning algorithm.

Aspects of the disclosure provide for automatically generating labels for sensor data. For instance first sensor data for a first vehicle is identified. The first sensor data is defined in both a global coordinate system and a local coordinate system for the first vehicle. A second vehicle is identified based on a second location of the second vehicle within a threshold distance of the first vehicle within the first timeframe. The second vehicle is associated with second sensor data that is further associated with a label identifying a location of an object, and the location of the object is defined in a local coordinate system of the second vehicle. A conversion from the local coordinate system of the second vehicle to the local coordinate system of the first vehicle may be determined and used to transfer the label from the second sensor data to the first sensor data.

Methods and systems are disclosed for improvements in aquaculture that allow for increasing the number and harvesting efficiency of crustaceans in an aquaculture setting by identifying and predicting internal conditions and/or physiological conditions of the crustaceans based on external characteristics that are imaged through non-invasive means.