System and method for feature visualization in a convolutional neural network

The disclosed technology provides methods for training of a convolutional neural network (CNN) to identify or predict its own errors, and then those errors are used as inputs with feature visualization to generate images of scenes associated with those errors. This allows adjustment of a set of labeled training images, and then the adjusted set of labeled training images are used to retrain or further train the CNN. Systems and machine-readable media are also provided.

BACKGROUND

1. Technical Field

The disclosed technology provides solutions for improving models used for object detection and identification in autonomous vehicles (AVs) and in particular, for visualizing features that contribute to errors in object recognition and identification in a convolutional neural network.

Autonomous vehicles (AVs) are vehicles having computers and control systems that perform driving and navigation tasks that are conventionally performed by a human driver. As AV technologies continue to advance, they will be increasingly used to improve transportation efficiency and safety. As such, AVs will need to perform many of the functions that are conventionally performed by human drivers, such as performing navigation and routing tasks necessary to provide a safe and efficient transportation. Such tasks may require the collection and processing of large quantities of data using various sensor types, including but not limited to cameras and/or Light Detection and Ranging (LiDAR) sensors disposed on the AV. In some instances, the collected data can be used by the AV to perform tasks relating to routing, planning and obstacle avoidance.

DETAILED DESCRIPTION

Perception systems of autonomous vehicles are designed to detect various objects in the surrounding environment in order to execute effective navigation and planning operations. These perception systems use models that have been trained using labeled data. In many instances, the labeled data is image-based, and includes camera, LiDAR, and/or radar data.

Once a model has been trained, it can be validated and used against a larger image-based data set that is often not labeled. Although the trained model may have been trained on certain objects, the performance may be less than desired. For example, it may fail to correctly detect and identify the object when it is in certain surrounding scenes. It is therefore desirable to understand what features associated with the object or in the surrounding scene may be contributing to errors in the detection and identification. Those features may be directly associated with the object, or they may only be correlated with the object. As an example, a model might have been trained to recognize wolves and huskies and to distinguish between them. Images labeled as huskies and images labeled as wolves would have been used for that training. Once trained, the model may perform well on unlabeled image data when the wolf is in a snowy environment, but the model may incorrectly identify a wolf as a huskie if there is no snow in the scene. In such an instance, the model may be using snow in the scene to distinguish between wolf and huskie. The snow is correlated with the wolf, but snow in the scene is not a feature of the wolf. Feature visualization may help to understand why the model is operating in this fashion, and that understanding can be used to improve the model. As an example, labeled images of wolves without snow in the scene might be used to further train the model, or the images of wolves with snow in the scene might be removed from the training data set and the model retrained without those snow images.

The idea of feature visualization is known, and the Nov. 7, 2017 paper by Chris Olah, Alexander Mordvintsev and Ludwig Schubert titled “Feature Visualization” How neural networks build up their understanding of images, which is available at https://distill.pub/2017/feature-visualization/, provides background on the idea and also provides examples. Some of that background and some examples in this specification are taken from or derived from that paper, which will be referred to hereinafter as “Olah”.

As described in Olah, it is possible to determine what kind of input will cause a particular behavior of a model. This can be done by the use of derivatives to iterate an input towards that goal. Conceptually, a single neuron or a channel of a trained model is/are iteratively stimulated, starting from an image of random noise, to generate an artificial image that represents the features triggering that single neuron or channel. This process may be referred to as optimization. It gives an example input image that causes the desired behavior. In a similar fashion, it is possible to discover the best and worst operating conditions of a model by differentiating the input with respect to model errors, as opposed to differentiating the loss with respect to model weights as might be typically done. As an example, this may allow a determination that a trained AV model has a high width error when vehicles are at 90 degrees to the AV.

As illustrated inFIG.1, if a model that has been trained on labeled images of animals, then feature visualization of neurons or channels for that model using optimization may generate an example image that appears to show animal faces and snouts of those animals. The concept of the instant invention is similar. By training a model to predict errors in the model, and then stimulating the CNN with those model errors, it is possible to generate images that provide a visualization of the model errors.

In another example illustrated inFIG.2A, a model may have been trained to recognize crosses on the tops of buildings. Feature visualization of the associated neuron or channel, generates an example image that appears to include a cross, but it also includes blue sky. In this example, the feature visualization helps to show that the model may be performing poorly in certain instances because it is using blue sky as a feature for recognition and identification, although the blue sky is only correlated with the cross on the top of the church.

With regard to the output image inFIG.2A, to improve the model performance, the labeled training image set may be adjusted to include more images of crosses on the tops of buildings without blue sky, or images with blue sky may be removed from the labeled training image set. With that change the model can be retrained, or further trained. In the instant invention, a similar concept is used with feature visualization to generate images associated with model errors, and then adjust the training image set to reduce the errors.

As described by Olah, one approach for feature visualization starts from an image with random noise. Iterative optimization from that random image, to maximally stimulate the desired neuron or channel proceeds through multiple steps. That iterative process may include jittering, rotation or scaling of the generated image between steps to reduce noise.FIG.2Billustrates an example of one such feature visualization.

FIG.3illustrates a flow diagram of an example process300, according to some aspects of the disclosed technology. At step302, the process300includes using a first set of labeled images to train a convolutional neural network (CNN). The first set of labeled images represent a first object in a plurality of scenes.

At step304, the process300includes training the CNN to predict a plurality of model errors. The predicted model errors could be from the same CNN, or a different CNN.

At step306, the process300includes identifying a first model error from the plurality of model errors. The identification or selection of the first model error could be to maximize the total model error, or it could be to identify a model error that contributes to the total model error, but is not the largest source of error.

At step308, the process300includes generating a first image by stimulating the CNN. The first image provides a visualization associated with the first model error.

At step310, the process300includes selecting a second set of labeled images based at least on the visualization of the first model error. This could include selecting images that deemphasize a feature that is included in the first image, or it could include selecting images that emphasize a feature that is included in the first image.

At step312, the process300includes using the second set of labeled images for additional training of the CNN. The additional training could be the addition or the subtraction of training images and then retraining the existing CNN with the revised training set, or it could be further training using the revised training images.

In the example ofFIG.1, a dog neuron of the CNN is stimulated. In the instant invention, the model is trained to predict its own error, and that error is used as an input to feature visualization. The invention is conceptually stimulating a neuron associated with a source of model error. This will generate scenes that represent a source of error, and those scenes can be used to identify areas where further training is needed.

As a further example, the largest model error might be associated with protected left turns where a large number of people are near by. Feature visualization, using that largest model error should generate images representing that type of scene (protected left turns where a large number of people are near by). If it is already known that this type of scene is a problem and it is being worked on, then another model error can be used as an input, and feature visualization may show a scene that was not already known to be causing problems. This allows the identification of difficult scenes that are error sources, whether they were previously known, or not. This can be considered as a regression on model error.

Uncertainty sampling is a known concept that can be used to prioritize data for training. Under this paradigm, an uncertainty metric is defined, and that metric used to capture data for which a model is least confident in its decision. Those examples are then prioritized for annotation and training. The instant invention similarly combines the concept of uncertainty sampling with feature visualization to identify scenes that can be prioritized for labeling and training.

In this example, the AV management system400includes an AV402, a data center450, and a client computing device470. The AV402, the data center450, and the client computing device470can communicate with one another over one or more networks (not shown), such as a public network (e.g., the Internet, an Infrastructure as a Service (IaaS) network, a Platform as a Service (PaaS) network, a Software as a Service (SaaS) network, other Cloud Service Provider (CSP) network, etc.), a private network (e.g., a Local Area Network (LAN), a private cloud, a Virtual Private Network (VPN), etc.), and/or a hybrid network (e.g., a multi-cloud or hybrid cloud network, etc.).

The AV402can navigate roadways without a human driver based on sensor signals generated by multiple sensor systems404,406, and408. The sensor systems404-408can include different types of sensors and can be arranged about the AV402. For instance, the sensor systems404-408can comprise Inertial Measurement Units (IMUs), cameras (e.g., still image cameras, video cameras, etc.), light sensors (e.g., LiDAR systems, ambient light sensors, infrared sensors, etc.), RADAR systems, GPS receivers, audio sensors (e.g., microphones, Sound Navigation and Ranging (SONAR) systems, ultrasonic sensors, etc.), engine sensors, speedometers, tachometers, odometers, altimeters, tilt sensors, impact sensors, airbag sensors, seat occupancy sensors, open/closed door sensors, tire pressure sensors, rain sensors, and so forth. For example, the sensor system404can be a camera system, the sensor system406can be a LiDAR system, and the sensor system408can be a RADAR system. Other embodiments may include any other number and type of sensors.

The AV402can also include several mechanical systems that can be used to maneuver or operate the AV402. For instance, the mechanical systems can include a vehicle propulsion system430, a braking system432, a steering system434, a safety system436, and a cabin system438, among other systems. The vehicle propulsion system430can include an electric motor, an internal combustion engine, or both. The braking system432can include an engine brake, brake pads, actuators, and/or any other suitable componentry configured to assist in decelerating the AV402. The steering system434can include suitable componentry configured to control the direction of movement of the AV402during navigation. The safety system436can include lights and signal indicators, a parking brake, airbags, and so forth. The cabin system438can include cabin temperature control systems, in-cabin entertainment systems, and so forth. In some embodiments, the AV402might not include human driver actuators (e.g., steering wheel, handbrake, foot brake pedal, foot accelerator pedal, turn signal lever, window wipers, etc.) for controlling the AV402. Instead, the cabin system438can include one or more client interfaces (e.g., Graphical User Interfaces (GUIs), Voice User Interfaces (VUIs), etc.) for controlling certain aspects of the mechanical systems430-438.

The AV402can additionally include a local computing device410that is in communication with the sensor systems404-408, the mechanical systems430-438, the data center450, and the client computing device470, among other systems. The local computing device410can include one or more processors and memory, including instructions that can be executed by the one or more processors. The instructions can make up one or more software stacks or components responsible for controlling the AV402; communicating with the data center450, the client computing device470, and other systems; receiving inputs from riders, passengers, and other entities within the AV's environment; logging metrics collected by the sensor systems404-408; and so forth. In this example, the local computing device410includes a perception stack412, a mapping and localization stack414, a prediction stack416, a planning stack418, a communications stack420, a control stack422, an AV operational database424, and an HD geospatial database426, among other stacks and systems.

The perception stack412can enable the AV402to “see” (e.g., via cameras, LiDAR sensors, infrared sensors, etc.), “hear” (e.g., via microphones, ultrasonic sensors, RADAR, etc.), and “feel” (e.g., pressure sensors, force sensors, impact sensors, etc.) its environment using information from the sensor systems404-408, the mapping and localization stack414, the HD geospatial database426, other components of the AV, and other data sources (e.g., the data center450, the client computing device470, third party data sources, etc.). The perception stack412can detect and classify objects and determine their current locations, speeds, directions, and the like. In addition, the perception stack412can determine the free space around the AV402(e.g., to maintain a safe distance from other objects, change lanes, park the AV, etc.). The perception stack412can also identify environmental uncertainties, such as where to look for moving objects, flag areas that may be obscured or blocked from view, and so forth. In some embodiments, an output of the prediction stack can be a bounding area around a perceived object that can be associated with a semantic label that identifies the type of object that is within the bounding area, the kinematic of the object (information about its movement), a tracked path of the object, and a description of the pose of the object (its orientation or heading, etc.).

The mapping and localization stack414can determine the AV's position and orientation (pose) using different methods from multiple systems (e.g., GPS, IMUs, cameras, LiDAR, RADAR, ultrasonic sensors, the HD geospatial database426, etc.). For example, in some embodiments, the AV402can compare sensor data captured in real-time by the sensor systems404-408to data in the HD geospatial database426to determine its precise (e.g., accurate to the order of a few centimeters or less) position and orientation. The AV402can focus its search based on sensor data from one or more first sensor systems (e.g., GPS) by matching sensor data from one or more second sensor systems (e.g., LiDAR). If the mapping and localization information from one system is unavailable, the AV402can use mapping and localization information from a redundant system and/or from remote data sources.

The prediction stack416can receive information from the localization stack414and objects identified by the perception stack412and predict a future path for the objects. In some embodiments, the prediction stack416can output several likely paths that an object is predicted to take along with a probability associated with each path. For each predicted path, the prediction stack416can also output a range of points along the path corresponding to a predicted location of the object along the path at future time intervals along with an expected error value for each of the points that indicates a probabilistic deviation from that point.

The planning stack418can determine how to maneuver or operate the AV402safely and efficiently in its environment. For example, the planning stack418can receive the location, speed, and direction of the AV402, geospatial data, data regarding objects sharing the road with the AV402(e.g., pedestrians, bicycles, vehicles, ambulances, buses, cable cars, trains, traffic lights, lanes, road markings, etc.) or certain events occurring during a trip (e.g., emergency vehicle blaring a siren, intersections, occluded areas, street closures for construction or street repairs, double-parked cars, etc.), traffic rules and other safety standards or practices for the road, user input, and other relevant data for directing the AV402from one point to another and outputs from the perception stack412, localization stack414, and prediction stack416. The planning stack418can determine multiple sets of one or more mechanical operations that the AV402can perform (e.g., go straight at a specified rate of acceleration, including maintaining the same speed or decelerating; turn on the left blinker, decelerate if the AV is above a threshold range for turning, and turn left; turn on the right blinker, accelerate if the AV is stopped or below the threshold range for turning, and turn right; decelerate until completely stopped and reverse; etc.), and select the best one to meet changing road conditions and events. If something unexpected happens, the planning stack418can select from multiple backup plans to carry out. For example, while preparing to change lanes to turn right at an intersection, another vehicle may aggressively cut into the destination lane, making the lane change unsafe. The planning stack418could have already determined an alternative plan for such an event. Upon its occurrence, it could help direct the AV402to go around the block instead of blocking a current lane while waiting for an opening to change lanes.

The control stack422can manage the operation of the vehicle propulsion system430, the braking system432, the steering system434, the safety system436, and the cabin system438. The control stack422can receive sensor signals from the sensor systems404-408as well as communicate with other stacks or components of the local computing device410or a remote system (e.g., the data center450) to effectuate operation of the AV402. For example, the control stack422can implement the final path or actions from the multiple paths or actions provided by the planning stack418. This can involve turning the routes and decisions from the planning stack418into commands for the actuators that control the AV's steering, throttle, brake, and drive unit.

The communication stack420can transmit and receive signals between the various stacks and other components of the AV402and between the AV402, the data center450, the client computing device470, and other remote systems. The communication stack420can enable the local computing device410to exchange information remotely over a network, such as through an antenna array or interface that can provide a metropolitan WIFI network connection, a mobile or cellular network connection (e.g., Third Generation (3G), Fourth Generation (4G), Long-Term Evolution (LTE), 5th Generation (5G), etc.), and/or other wireless network connection (e.g., License Assisted Access (LAA), Citizens Broadband Radio Service (CBRS), MULTEFIRE, etc.). The communication stack420can also facilitate the local exchange of information, such as through a wired connection (e.g., a user's mobile computing device docked in an in-car docking station or connected via Universal Serial Bus (USB), etc.) or a local wireless connection (e.g., Wireless Local Area Network (WLAN), Bluetooth®, infrared, etc.).

The AV operational database424can store raw AV data generated by the sensor systems404-408, stacks412-422, and other components of the AV402and/or data received by the AV402from remote systems (e.g., the data center450, the client computing device470, etc.). In some embodiments, the raw AV data can include HD LiDAR point cloud data, image data, RADAR data, GPS data, and other sensor data that the data center450can use for creating or updating AV geospatial data or for creating simulations of situations encountered by AV402for future testing or training of various machine learning algorithms that are incorporated in the local computing device410.

The data center450can be a private cloud (e.g., an enterprise network, a co-location provider network, etc.), a public cloud (e.g., an Infrastructure as a Service (IaaS) network, a Platform as a Service (PaaS) network, a Software as a Service (SaaS) network, or other Cloud Service Provider (CSP) network), a hybrid cloud, a multi-cloud, and so forth. The data center450can include one or more computing devices remote to the local computing device410for managing a fleet of AVs and AV-related services. For example, in addition to managing the AV402, the data center450may also support a ridesharing service, a delivery service, a remote/roadside assistance service, street services (e.g., street mapping, street patrol, street cleaning, street metering, parking reservation, etc.), and the like.

The data center450can send and receive various signals to and from the AV402and the client computing device470. These signals can include sensor data captured by the sensor systems404-408, roadside assistance requests, software updates, ridesharing pick-up and drop-off instructions, and so forth. In this example, the data center450includes a data management platform452, an Artificial Intelligence/Machine Learning (AI/ML) platform454, a simulation platform456, a remote assistance platform458, and a ridesharing platform460, among other systems.

The AI/ML platform454can provide the infrastructure for training and evaluating machine learning algorithms for operating the AV402, the simulation platform456, the remote assistance platform458, the ridesharing platform460, the cartography platform462, and other platforms and systems. Using the AI/ML platform454, data scientists can prepare data sets from the data management platform452; select, design, and train machine learning models; evaluate, refine, and deploy the models; maintain, monitor, and retrain the models; and so on.

The simulation platform456can enable testing and validation of the algorithms, machine learning models, neural networks, and other development efforts for the AV402, the remote assistance platform458, the ridesharing platform460, the cartography platform462, and other platforms and systems. The simulation platform456can replicate a variety of driving environments and/or reproduce real-world scenarios from data captured by the AV402, including rendering geospatial information and road infrastructure (e.g., streets, lanes, crosswalks, traffic lights, stop signs, etc.) obtained from the cartography platform462; modeling the behavior of other vehicles, bicycles, pedestrians, and other dynamic elements; simulating inclement weather conditions, different traffic scenarios; and so on.

The remote assistance platform458can generate and transmit instructions regarding the operation of the AV402. For example, in response to an output of the AI/ML platform454or other system of the data center450, the remote assistance platform458can prepare instructions for one or more stacks or other components of the AV402.

The ridesharing platform460can interact with a customer of a ridesharing service via a ridesharing application472executing on the client computing device470. The client computing device470can be any type of computing system, including a server, desktop computer, laptop, tablet, smartphone, smart wearable device (e.g., smartwatch, smart eyeglasses or other Head-Mounted Display (HMD), smart ear pods, or other smart in-ear, on-ear, or over-ear device, etc.), gaming system, or other general purpose computing device for accessing the ridesharing application472. The client computing device470can be a customer's mobile computing device or a computing device integrated with the AV402(e.g., the local computing device410). The ridesharing platform460can receive requests to pick up or drop off from the ridesharing application472and dispatch the AV402for the trip.

FIG.5illustrates an example processor-based system with which some aspects of the subject technology can be implemented. For example, processor-based system500can be any computing device making up local computing device410, client computing device470, a passenger device executing the rideshare app472, data center450, or any component thereof in which the components of the system are in communication with each other using connection505. Connection505can be a physical connection via a bus, or a direct connection into processor510, such as in a chipset architecture. Connection505can also be a virtual connection, networked connection, or logical connection.

Example system500includes at least one processing unit (CPU or processor)510and connection505that couples various system components including system memory515, such as read-only memory (ROM)520and random-access memory (RAM)525to processor510. Computing system500can include a cache of high-speed memory512connected directly with, in close proximity to, or integrated as part of processor510.

Processor510can include any general-purpose processor and a hardware service or software service, such as services532,534, and536stored in storage device530, configured to control processor510as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor510may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.

Storage device530can include software services, servers, services, etc., that when the code that defines such software is executed by the processor510, it causes the system to perform a function. In some embodiments, a hardware service that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor510, connection505, output device535, etc., to carry out the function.

As understood by those of skill in the art, machine-learning based classification techniques can vary depending on the desired implementation. For example, machine-learning classification schemes can utilize one or more of the following, alone or in combination: hidden Markov models; recurrent neural networks; convolutional neural networks (CNNs); deep learning; Bayesian symbolic methods; general adversarial networks (GANs); support vector machines; image registration methods; applicable rule-based system. Where regression algorithms are used, they may include but are not limited to: a Stochastic Gradient Descent Regressor, and/or a Passive Aggressive Regressor, etc.

Illustrative examples of the disclosure include:

Aspect 1. A computer-implemented method for training a convolutional neural network (CNN), the method comprising: using a first set of labeled images to train the CNN, the first set of labeled images representing a first object in a plurality of scenes; training the CNN to predict a plurality of model errors; identifying a first model error from the plurality of model errors; generating a first image by stimulating the CNN, the first image providing a first visualization associated with the first model error; selecting a second set of labeled images based at least on the first visualization; and using the second set of labeled images for additional training of the CNN.

Aspect 2. The computer-implemented method of Aspect 1, further comprising: selecting the second set of labeled images to emphasize the first visualization associated with the first model error.

Aspect 3. The computer-implemented method of any of Aspects 1-2, further comprising: selecting the second set of labeled images to de-emphasize the first visualization associated with the first model error.

Aspect 4. The computer-implemented method of any of Aspects 1-3, further comprising: training a second CNN to predict the plurality of model errors; and identifying the first model error using the second CNN.

Aspect 5. The computer-implemented method of any of Aspects 1-4, further comprising: identifying a second model error from the plurality of model errors, the second model error different from the first model error; generating a second image by stimulating the CNN, the second image providing a second visualization associated with the second model error; selecting a third set of labeled images based at least on the second visualization of the second model error; and using the third set of labeled images for further training of the CNN.

Aspect 6. The computer-implemented method of any of Aspects 1-5, further comprising: selecting the second set of images by removing one or more images from the first set of labeled images based at least on the first visualization associated with the first model error; and using the second set of images to retrain the CNN.

Aspect 7. The computer-implemented method of any of Aspects 1-6, further comprising: selecting the second set of images by adding one or more images to the first set of labeled images based at least on the first visualization associated with the first model error; and using the second set of images to retrain the CNN.

Aspect 8. The computer-implemented method of any of Aspects 1-7, further comprising determining that the first model error in the plurality of model errors is associated with a maximum model error.

Aspect 9. An apparatus for training a convolutional neural network (CNN), the apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor configured to: use a first set of labeled images to train the CNN, the first set of labeled images representing a first object in a plurality of scenes; train the CNN to predict a plurality of model errors; identify a first model error from the plurality of model errors; generate a first image by stimulating the CNN, the first image providing a first visualization associated with the first model error; select a second set of labeled images based at least on the first visualization; and use the second set of labeled images for additional training of the CNN.

Aspect 10. The apparatus of Aspect 9, wherein the processor is further configured to select the second set of labeled images to emphasize the first visualization associated with the first model error.

Aspect 11. The apparatus of any of Aspects 9-10, wherein the processor is further configured to select the second set of labeled images to de-emphasize the first visualization associated with the first model error.

Aspect 12. The apparatus of any of Aspects 9-11, wherein the processor is further configured to train a second CNN to predict the plurality of model errors; and identify the first model error using the second CNN.

Aspect 13. The apparatus of any of Aspects 9-12, wherein the processor is further configured to: identify a second model error from the plurality of model errors, the second model error different from the first model error; generate a second image by stimulating the CNN, the second image providing a second visualization associated with the second model error; select a third set of labeled images based at least on the second visualization of the second model error; and use the third set of labeled images for further training of the CNN.

Aspect 14. The apparatus of any of Aspects 9-13, wherein the processor is further configured to: select the second set of images by removing one or more images from the first set of labeled images based at least on the first visualization associated with the first model error; and use the second set of images to retrain the CNN.

Aspect 15. The apparatus of any of Aspects 9-14, wherein the processor is further configured to: select the second set of images by adding one or more images to the first set of labeled images based at least on the first visualization of the first model error; and use the second set of images to retrain the CNN.

Aspect 16. The apparatus of any of Aspects 9-15, wherein the processor is further configured to determine that the first model error in the plurality of model errors is associated with a maximum model error.

Aspect 17. A non-transitory computer-readable storage medium comprising at least one instruction for causing a computer or processor to: use a first set of labeled images to train a convolutional neural network (CNN), the first set of labeled images representing a first object in a plurality of scenes; train the CNN to predict a plurality of model errors; identify a first model error from the plurality of model errors; generate a first image by stimulating the CNN, the first image providing a first visualization associated with the first model error; select a second set of labeled images based at least on the first visualization; and use the second set of labeled images for additional training of the CNN.

Aspect 18. The non-transitory computer-readable storage medium of Aspect 17, wherein the at least one instruction is further configured to cause the processor to select the second set of labeled images to emphasize the first visualization associated with the first model error.

Aspect 19. The non-transitory computer-readable storage medium of any of Aspects 17-18, wherein the at least one instruction is further configured to cause the processor to select the second set of labeled images to de-emphasize the first visualization associated with the first model error.

Aspect 20. The non-transitory computer-readable storage medium of any of Aspects 17 to 19, wherein the at least one instruction is further configured to cause the processor to: identify a second model error from the plurality of model errors, the second model error different from the first model error; generate a second image by stimulating the CNN, the second image providing a second visualization associated with the second model error; select a third set of labeled images based at least on the second visualization of the second model error; and use the third set of labeled images for further training of the CNN.

Aspect 21. A system comprising means for performing a method according to any of Aspects 1 to 8.