A computer implemented method, computer system and computer program product are provided for data classification. The method includes receiving original data, wherein the original data includes at least one object in a first condition. The method also includes receiving generated data from a generator based on the original data, wherein the generated data includes the at least one object in a second condition, the generator trained by training data of the first condition and training data of the second condition. The method further includes determining a classification of the at least one object with a classifier based on the original data and the generated data, the classifier trained by labeled data of the first condition and more training data of the second condition that is generated based on the labeled data by the trained generator, wherein the labeled data includes the at least one object.

According to one embodiment, an image processing apparatus 1 includes one or more hardware processors configured to function as an acquisition unit 20A, a pseudo label estimation unit 20B, and a learning unit 20C. The acquisition unit 20A acquires unlabeled training data including an image to which a correct label of an attribute is unassigned. The pseudo-label estimation unit 20B estimates a pseudo-label, which is an estimation result of the attribute of the image of the unlabeled training data, based on an identification target region according to a type of the attribute to be identified by a first learning model 30 to be learned in the image of the unlabeled training data. The learning unit 20C learns the first learning model 30 identifying the attribute of the image by using first labeled training data with the pseudo-label being assigned to the image of the unlabeled training data.

A system determines an input video and a first annotated image from the input video which identifies an object of interest. The system initiates a tracker based on the first annotated image and the input video. The tracker generates, based on the first annotated image and the input video, information including: a sliding window for false positives; a first set of unlabeled images from the input video; and at least two images with corresponding labeled states. A semi-supervised classifier classifies, based on the information, the first set of unlabeled images from the input video. If a first unlabeled image is classified as a false positive, the system reinitiates the tracker based on a second annotated image occurring in a frame prior to a frame with the false positive. The system generates an output video comprising the input video displayed with tracking on the object of interest.

Techniques for implementing unsupervised anomaly detection by self-prediction are provided. In one set of embodiments, a computer system can receive an unlabeled training data set comprising a plurality of unlabeled data instances, where each unlabeled data instance includes values for a plurality of features. The computer system can further train, for each feature in the plurality of features, a supervised machine learning (ML) model using a labeled training data set derived from the unlabeled training data set, receive a query data instance, and generate a self-prediction vector using at least a portion of the trained supervised ML models and the query data instance, where the self-prediction vector indicates what the query data instance should look like if it were normal. The computer system can then generate an anomaly score for the query data instance based on the self-prediction vector and the query data instance.

A method and system provide for augmenting a photograph. An unlabeled photograph is obtained. A weakly augmented photograph and a strongly augmented photograph are obtained from the unlabeled photograph based on different types of data augmentation methods. The weakly augmented photograph is processed through a model to generate multiple weakly augmented photograph class predictions (with assigned probabilities). The multiple weakly augmented photograph class predictions are converted into positive pseudo-labels (indicating a presence of a class) or negative pseudo-labels (indicating absence of a class) using different fixed percentile thresholds. The strongly augmented photograph is processed through the model to generate a strongly augmented photograph class prediction. The model is trained to make the strongly augmented photograph label prediction match the positive pseudo-label via a cross-entropy loss. The trained model is then utilized to label the unlabeled photograph with multiple labels.

Methods and systems for training a model include pre-training a backbone model with a pre-training decoder, using an unlabeled dataset with multiple distinct sensor data modalities that derive from different sensor types. The backbone model is fine-tuned with an output decoder after pre-training, using a labeled dataset with the multiple modalities.

A method, an apparatus, device and a storage medium for generating a target re-recognition model are provided. The method may include: acquiring a set of labeled samples, a set of unlabeled samples and an initialization model obtained through supervised training; performing feature extraction on each sample in the set of the unlabeled samples by using the initialization model; clustering features extracted from the set of the unlabeled samples by using a clustering algorithm; assigning, for each sample in the set of the unlabeled samples, a pseudo label to the sample according to a cluster corresponding to the sample in a feature space; and mixing a set of samples with a pseudo label and the set of the labeled samples as a set of training samples, and performing supervised training on the initialization model to obtain a target re-recognition model.

Various examples described herein include image-processing tasks of image data used for defect detection and comparison of features on substrates. At least one of a deep-convnet-based and a transformer-based backbone network (a common backbone) that is arranged to convert various types of raw-image data into features that can, in turn, be used by smaller neural networks to perform final calculations of specific tasks. The smaller neural networks perform, for example, final defect-detections, die-to-die image comparisons, anomaly detection, and customer-specific tasks in a fabrication facility. The common-backbone network can initially be trained using self-supervised learning based on the raw-image data, and then transfer learning can be used to train a final application of the task-specific networks. Other systems and methods are also disclosed.

A computing system includes a processor that executes program instructions and memory for storing the program instructions. The program instructions include an artificial neural network (ANN) that receives input data. The ANN maps the input data to a latent representation of the input data. The ANN maps the latent representation of the input data to a reconstruction of the input data. The computing system adapts learning features of an artificial intelligence model based on an output of the ANN.

An image detection method and apparatus are disclosed. The method includes: performing feature extraction processing on the image to obtain a feature representation subset of the image; generating attention weights corresponding to the at least two sub-image features; performing weighting aggregation processing on the at least two sub-image features according to the attention weights to obtain a first feature vector; performing clustering sampling processing on the at least two sub-image features to obtain at least two classification clusters comprising sampled sub-image features; determining a block sparse self-attention for each of the sampled sub-image features according to the at least two classification clusters and a block sparse matrix; determining a second feature vector according to at least two block sparse self-attentions respectively corresponding to the at least two classification clusters; and determining a classification result of the image according to the first feature vector and the second feature vector.