Patent ID: 12254675

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Machine learning systems, in particular, neural networks, are usually trained with the aid of a so-called gradient descent method. Gradient descent methods are characterized in that parameters, in particular, weights of the machine learning system, are iteratively updated in every training step as a function of a calculated gradient. In this case, the gradients are ascertained via a derivation of a cost function l, the cost function therefore being evaluated on training data and being derived via the parameters of the machine learning system. For the usual gradient descent method, cost function l(θ) is a function of parameters θ of the machine learning system, as well as of ascertained output variables of the machine learning system and provided target output variables, in particular, labels.

The present invention begins here in this training method with gradient descent methods and supplements this training method as explained below and as schematically represented inFIG.1.

At start S1of the training method, metaparameters ϕ, in addition to parameters θ of the machine learning system, are also initialized. It is noted that here two successive metaparameters for the first training steps may be initialized: for example, ϕ1, ϕ2:=ϕ1

Metaparameter ϕ parameterizes, for example, a data augmentation of the training data, for example, a distribution via distortions of the images or via rotations.

Cost function l(θ, ϕ) is also expanded in such a way that the cost function is now also a function of metaparameter ϕ.

Actual training step S2of the machine learning system, in which parameters θ are updated as a function of the gradient, remains unchanged. This means, a gradient ∇θl(θ, ϕ) with respect to metaparameter θ is calculated via cost function l(θ, ϕ), the cost function being evaluated using the instantaneous parameters of instantaneous iteration step t on the respective used training data: l(θt, ϕt).

This is followed, in contrast to the usual training method, by an additional optimization step S3. In this step, metaparameter ϕ is optimized via an additional gradient descent method. For this purpose, a gradient ∇ϕt−1with respect to the metaparameter is calculated as a function of the cost function, for this purpose, the cost function being evaluated as a function of metaparameter ϕt−1used in immediately preceding training step t−1: l(θt, ϕt−1). This means, instantaneous metaparameter ϕtis updated as a function of the value of preceding metaparameter ϕt−1.

This adaptation of the metaparameter enabled in such a way between two training iterations t−1,t effectively means that immediately preceding metaparameter ϕt−1used on the instantaneously used training data, which have been used for ascertaining the cost function with the instantaneous parameters of the machine learning system, is evaluated. This generates a dependency between successive steps as opposed to the usual training method. Via this further dependency, additional optimization step S3for optimizing metaparameter ϕtresults in the metaparameter being optimized in such a way that when used in the next training step, the metaparameter further minimizes the cost function. As a result, it may be said that a more rapid convergence due to the metaparameter is achieved by this newly introduced dependency, since the metaparameter advantageously influences the optimization of the cost function usually carried out.

Once metaparameter ϕt+1has then been set for the next training step S4: ϕt−1ζϕt−β∇ϕt−1l, and the parameters of the machine learning system for the next training have also been set: θt+1ζθt−α∇θtl, training steps S2and S3just described are carried out again, in particular, carried out multiple times in succession, until a predefined abort criterion is met. It is noted that parameters α,β represent weightings of the gradients. These parameters preferably have a value between 0≤α, β<1.

It is noted that in the subsequent training steps before carrying out step S2, the training data are augmented in each case as a function of the set metaparameter. It has been found in experiments, however, that the augmentation of the training data has resulted in significant performance improvements only in every n-th training step. Preferably, n=2 is selected here. In one further exemplary embodiment, the gradient descent method for the machine learning system or a structure of the machine learning system may alternatively or in addition be changed after step S2as a function of the metaparameter.

If the training has been completed by a multiple sequential repetition of step S2and S3, step S4may follow. Herein, the machine learning system just trained is output.

In a subsequent step S5, the output machine learning system may then be used, for example, to control an actuator. In this case, the machine learning system is able to process data provided to it and the actuator is then activated as a function of the ascertained result of the machine learning system.

In one preferred exemplary embodiment, the machine learning system is trained using images in order to classify/segment objects in the images.

In order to further improve the training method, gradient ∇ϕt−1l is determined using the REINFORCE trick. This measure has the advantage that with this trick, non-differentiable metaparameters ϕ are optimizable, for example, because the latter are not constant or because the latter are characterized by a non-constant probability distribution p.

For example, distribution p may be a function of metaparameter ϕ and may output a value αi˜p(⋅; ϕ) for training data point i. For example, αimay characterize a value of a hyperparameter of the machine learning system (for example, dropout rate) or a training data point selection strategy. Distribution p(⋅; ϕ) may, for example, be a Softmax distribution, which is parameterized by ϕ.

For the measure just cited, a scalar product is used, which connects two successive batches of training data. The scalar product is ascertained as follows for the i-th training data point:
rt,i=∇θl(θt−1,ϕt)i,∇θL(θt)(Equation 2):
with l( )ibeing the cost function for the i-th training data point, in particular, from the respectively considered batch of training data points containing n-th training data point, and

L⁡(θt)=1n⁢∑j=1nl⁡(θt,ϕt)
being the cost function of the entire batch of immediately following step t and the scalar product,.

It is provided that scalar product rt,iis to be interpreted as a reward and the REINFORCE trick is to be applied thereto. Thus, gradient ∇ϕt−1ltmay now be approximated as follows:

(Equation 3):

∇ϕt-1lt≈∑i=1nrt,i·∇ϕtlog⁢p⁡(ai;ϕt)

FIG.2illustrates the temporal dependencies for ascertaining Equation 2 by way of example for successive steps t=1,2, . . . ,4.

FIG.3schematically shows an actuator10in its surroundings in interaction with a control system40. The surroundings are detected at preferably regular temporal intervals in a sensor30, in particular, in an imaging sensor such as a video sensor, which may also be provided by a plurality of sensors, for example, a stereo camera. Other imaging sensors are also possible such as, for example, radar, ultrasound or LIDAR. An infrared camera is also possible. Sensor signal S—or in the case of multiple sensors one sensor signal S each—of sensor30is transferred to control system40. Thus, control system40receives a sequence of sensor signals S. Control system40ascertains activation signals A therefrom, which are transferred to actuator10.

Control system40receives the sequence of sensor signals S of sensor30in an optional receiving unit50, which converts the sequence of sensor signals S into a sequence of input images x (alternatively, each sensor signal S may also be directly adopted as an input image x). Input image x may, for example, be a section or a further processing of sensor signal S. Input image x includes individual frames of a video recording. In other words, input image x is ascertained as a function of sensor signal S. The sequence of input images x is fed to a machine learning system, in the exemplary embodiment, the output machine learning system60from step S4.

Machine learning system network60ascertains output variables y from input images x. These output variables y may include, in particular, a classification and/or a semantic segmentation of input images x. Output variables y are fed to an optional forming unit, which ascertains therefrom activation signals A, which are fed to actuator10in order to activate actuator10accordingly. Output variable y includes pieces of information about objects detected by sensor30.

Actuator10receives control signals A, is activated accordingly and carries out a corresponding action. Actuator10in this case may include a (not necessarily structurally integrated) control logic, which ascertains from activation signal A a second activation signal, with which actuator10is then activated.

In one further specific embodiment, control system40includes sensor30. In still further specific embodiments, control system40also includes alternatively or in addition actuator10.

In further preferred specific embodiments, control system40includes a single or a plurality of processors45and at least one machine-readable memory medium46, on which the instructions are stored which, when they are carried out on processors45, then prompt control system40to carry out the method according to the present invention.

A display unit10aalternatively or in addition to actuator10is provided in alternative specific embodiments.

In one further exemplary embodiment, control system40is used for controlling an at least semi-autonomous robot, here, an at least semi-autonomous motor vehicle100. Sensor30may, for example, be a video sensor situated preferably in motor vehicle100.

Machine learning system60is preferably configured for the purpose of safely identifying x objects from the input images. Machine learning system60may be a neural network.

Actuator10situated preferably in motor vehicle100may, for example, be a brake, a drive or a steering system of motor vehicle100. Activation signal A may then be ascertained in such a way that the actuator or actuators10is/are activated in such a way that motor vehicle100prevents, for example, a collision with objects reliably identified by artificial neural network60, in particular, when objects of particular classes, for example, pedestrians, are involved.

Alternatively, the at least semi-autonomous robot may also be another mobile robot (not depicted), for example, one which moves by flying, floating, diving or pacing. The mobile robot may, for example, also be an at least semi-autonomous lawn mower or an at least semi-autonomous cleaning robot. In these cases as well, activation signal A may be ascertained in such a way that the drive and/or the steering system of the mobile robot is/are activated in such a way that the at least semi-autonomous robot prevents, for example, a collision with objects identified by artificial neural network60.

Alternatively or in addition, display unit10amay be activated with activation signal A and, for example, the ascertained safe areas may be displayed. It is also possible, for example, in a motor vehicle100including a non-automated steering system that display unit10ais activated with activation signal A in such a way that it outputs a visual or acoustic warning signal when it is ascertained that motor vehicle100threatens to collide with one of the reliably identified objects.

FIG.3shows one exemplary embodiment, in which control system40is used for activating a manufacturing machine11of a manufacturing system200by activating an actuator10that controls this manufacturing machine11. Manufacturing machine11may, for example, be a machine for stamping, sawing, drilling and/or cutting.

Sensor30may then, for example, be a visual sensor, which detects, for example, properties of manufacturing products12a,12b. It is possible that these manufacturing products12a,12b, are movable. It is possible that actuator10controlling manufacturing machine11is activated as a function of an assignment of detected manufacturing products12a,12b, so that manufacturing machine11correspondingly carries out a subsequent processing step of the correct one of manufacturing products12a,12b. It is also possible that by identifying the correct properties of the same one of manufacturing products12a,12b(i.e., without a misclassification), manufacturing machine11correspondingly adapts the same manufacturing step for a processing of a subsequent manufacturing product.

FIG.5shows one exemplary embodiment, in which control system40is used for controlling an access system300. Access system300may include a physical access control, for example, a door401. Video sensor30is configured to detect a person. This detected image may be interpreted with the aid of object identification system60. If multiple persons are detected simultaneously, the identity of the persons, for example, may be particularly reliably ascertained by an assignment of the persons (i.e., of the objects) relative to one another, for example, by an analysis of their movements. Actuator10may be a lock, which blocks or does not block the access control, as a function of activation signal A, for example, opens or does not open door401. For this purpose, activation signal A may be selected as a function of the interpretation of object identification system60, for example, as a function of the ascertained identity of the person. Instead of the physical access control, a logical access control may also be provided.

FIG.6shows one exemplary embodiment, in which control system40is used for controlling a monitoring system400. This exemplary embodiment differs from the exemplary embodiment shown inFIG.5in that instead of actuator10, display unit10ais provided, which is activated by control system40. For example, an identity of the objects recorded by video sensor30may be reliably ascertained by artificial neural network60in order, for example, to deduce therefrom which become suspicious, and activation signal A is then selected in such a way that this object is displayed in a color highlighted manner by display unit10a.

FIG.7shows one exemplary embodiment, in which control system40is used for controlling a personal assistant250. Sensor30is preferably a visual sensor, which receives images of a gesture of a user249.

Control system40ascertains as a function of the signals of sensor30an activation signal A of personal assistant250, for example, by the neural network carrying out a gesture recognition. This ascertained activation signal A is then conveyed to personal assistant250and the latter is thus activated accordingly. This ascertained activation signal A may, in particular, be selected in such a way that it corresponds to an assumed desired activation by user249. This assumed desired activation may be ascertained as a function of the gesture recognized by artificial neural network60. Control system40may then select activation signal A for conveyance to personal assistant250as a function of the assumed desired activation and/or may select activation signal A for conveyance to the personal assistant according to assumed desired activation250.

This corresponding activation may, for example, include that personal assistant250retrieves pieces of information from a database and reproduces them in an apprehensible manner for user249.

Instead of personal assistant250, a household appliance (not depicted), in particular, a washing machine, a stove, an oven, a microwave or a dishwasher may also be provided in order to be activated accordingly.

FIG.8shows one exemplary embodiment, in which control system40is used for controlling a medical imaging system500, for example, an MRI, an x-ray device or an ultrasound device. Sensor30may, for example, be provided in the form an imaging sensor, display unit10abeing activated by control system40. For example, it may be ascertained by neural network60whether an area recorded by the imaging sensor is conspicuous, and activation signal A may then be selected in such a way that this area is displayed in a color highlighted manner by display unit10a.