Robert Boschgmbh

Robert Bosch Gmbh

Wenbin He

Liang Gou

Liu Ren

Lincan Zou

Zijie Wang

A visual analytics workflow and system are disclosed for assessing, understanding, and improving deep neural networks. The visual analytics workflow advantageously enables interpretation and improvement of the performance of a neural network model, for example an image-based objection detection and classification model, with minimal human-in-the-loop interaction. A data representation component extracts semantic features of input image data, such as colors, brightness, background, rotation, etc. of the images or objects in the images. The input image data are passed through the neural network to obtain prediction results, such as object detection and classification results. An interactive visualization component transforms the prediction results and semantic features into interactive and human-friendly visualizations, in which graphical elements encoding the prediction results are visually arranged depending on the extracted semantic features of input image data.

Visual analytics system to assess, understand, and improve deep neural networks

A computer-implemented system and method includes generating first pseudo segment data from a first augmented image and generating second pseudo segment data from a second augmented image. The first augmented image and the second augmented image are in a dataset along with other augmented images. A machine learning system is configured to generate pixel embeddings based on the dataset. The first pseudo segment data and the second pseudo segment data are used to identify a first set of segments that a given pixel belongs with respect to the first augmented image and the second augmented image. A second set of segments is identified across the dataset. The second set of segments do not include the given pixel. A local segmentation loss is computed for the given pixel based on the corresponding pixel embedding that involves attracting the first set of segments while repelling the second set of segments. The global concept loss is computed based on a similarity determination between the first set of segments and a concept vector of a corresponding concept. The corresponding concept categorizes the first set of segments with other sets of segments across the dataset based on semantic meaning. The parameters of machine learning system are updated based on the total loss that takes into account at least the local segmentation loss and the global concept loss.

System and method with visual concept framework for self-supervised semantic segmentation

Methods and systems for performing function testing for moveable objects. One system includes an electronic processor configured to access a driving scene including a moveable object. The electronic processor is also configured to perform spatial representation learning on the driving scene. The electronic processor is also configured to generate an adversarial example based on the learned spatial representation. The electronic processor is also configured to retrain the deep learning model using the adversarial example and the driving scene.

Function testing for movable objects in safety critical applications with spatial representation learning and adversarial generation

Methods and systems for performing concept-based adversarial generation with steerable and diverse semantics. One system includes an electronic processor configured to access an input image. The electronic processor is also configured to perform concept-based semantic image generation based on the input image. The electronic processor is also configured to perform concept-based semantic adversarial learning using a set of semantic latent spaces generated as part of performing the concept-based semantic image generation. The electronic processor is also configured to generate an adversarial image based on the concept-based semantic adversarial learning. The electronic processor is also configured to test a target model using the adversarial image.

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Sunnyvale, CA, United States of America

Wenbin He