An image processing method and apparatus based on machine learning are disclosed. The image processing method based on machine learning, according to the present invention, may comprise the steps of: generating a first corrected image by inputting an input image to a first convolution neural network; generating an intermediate image on the basis of the input image; performing machine learning on a first loss function of the first convolution neural network on the basis of the first corrected image and the intermediate image; and performing machine learning on a second loss function of the first convolution neural network on the basis of the first corrected image and a natural image.

Quality associated with an interpretation of data captured as unstructured data can be determined. Attributes can be identified within the unstructured data automatically. Subsequently, sentiment associated with each of the attributes can be determined based on the unstructured data. Correctness of the unstructured data, and thus the interpretation, can be assessed based on a comparison of the attribute and associated sentiment with structured data. A quality score can be generated that captures the quality of the data interpretation in terms of correctness and as well as results of another analysis including completeness, among others. Comparison of the quality score to a threshold can dictate whether or not the interpretation is subject to further review.

An information processing apparatus that executes active learning by repeating image selection and retraining of a learning model with the selected images includes an acquisition unit configured to acquire a trained learning model, a first selection unit configured to select an image transformation method executed on an image by using the acquired learning model, and a second selection unit configured to select an image used to retrain the learning model by using the selected image transformation method and the acquired learning model.

The disclosed technology is generally directed to identifying media documents embedded within other media documents. In one example of the technology, source fingerprints are generated from input images using a source machine-learning model. The input images are derived from the media documents. Target fingerprints are generated from the input images using a target machine-learning model. The source machine-learning model includes a first neural network. The target machine-learning model includes a second neural network that is different from the first neural network. The source machine-learning model was trained in parallel with the target machine-learning model. Candidate media-document pairs from the media documents are determined based on the source fingerprints and the target fingerprints. Each candidate media-document pair includes a media document that is a candidate for being embedded in another media document.

A control unit is disclosed to control an imaging unit to perform imaging of a tray/container. The control unit can cause the performance of actions on the container using automated machines and/or directing humans to perform an action. For example, the presence of contamination in a container can be detected based on an image of the container captured by an imaging unit. The control unit can receive an image of the container from the imaging unit, determine whether the container is contaminated, and direct the container to a cleaning unit. Moreover, the control unit can detect a product based on an image of the product, determine an identity of the product based on the received image, and command an indicating unit to indicate a failure to determine the identity of the product.

Bias in Machine Learning (ML) is when an ML algorithm tends to incompletely learn relevant and important patterns from a dataset, or learns the patterns from data incorrectly. Such inaccuracy can cause the algorithm to miss important relationships between patterns and features in data, resulting in inaccurate algorithm predictions. Systems and methods for detecting potential ML bias in input image datasets are described herein. After a target image is received, a subset of images related to the target image is extracted. The target image and subset of images are analyzed under an imbalance assessment and data bias assessment to determine the presence of any potential data bias in a ML training pipeline. If any data bias is determined, one or more messages summarizing the assessments and including explanations to enable more accurate predictions in image assessments are sent to the user.

A method and system of enhancing online reflected light ferrograph images. The method includes: based on contour markers of wear particles in the online reflected light ferrograph image, performing concatenate fusion on the SqueezeNet-Unet-based wear particle position prediction network and the ResNeXt-CycleGAN image transformation network to construct an online reflected light ferrograph image enhancement model; determining loss function of the position prediction network; combining SSIM and L1 losses to optimize cycle-consistency loss function of the ResNeXt-CycleGAN image transformation network; designing overall loss function of the ferrograph image enhancement model by weighted fusion; and optimizing the ferrograph image enhancement model with the overall loss function as optimization object successively using a training sample set consisting of an original online reflected light ferrograph image and a traditional algorithm-enhanced online reflected light ferrograph image and a training sample set consisting of the original image and an offline reflected light ferrograph image.

A method may include receiving a number of images to train a first neural network, masking a portion of each of the images and inputting the masked images to the first neural network. The method may also include generating, by the first neural network, probable pixel values for pixels located in the masked portion of each of the plurality of images, forwarding the images including the probable pixel values to a second neural network and determining, by the second neural network, whether each of the probable pixel values is contextually suitable. The method may further include identifying pixels in each of the plurality of images that are not contextually suitable.

Techniques are provided for increasing the accuracy of automated classifications produced by a machine learning engine. Specifically, the classification produced by a machine learning engine for one photo-realistic image is adjusted based on the classifications produced by the machine learning engine for other photo-realistic images that correspond to the same portion of a 3D model that has been generated based on the photo-realistic images. Techniques are also provided for using the classifications of the photo-realistic images that were used to create a 3D model to automatically classify portions of the 3D model. The classifications assigned to the various portions of the 3D model in this manner may also be used as a factor for automatically segmenting the 3D model.

A novel system and method for accurate detection and quantification of fibrous tissue produces a virtual medical image of tissue treated with a second stain based on a received medical image of tissue treated with a first stain using a computer-implemented trained deep learning model. The model is trained to learn the deep texture patterns associated with collagen fibers using conditional generative adversarial networks to detect and quantify fibrous tissue.