US 12,705,541 B2Granted

Device and method for training a variational autoencoder

AssigneeRobert Bosch GmbH

InventorsFaris Janjos, Lars Rosenbaum, Maxim Dolgov

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Abstract

A computer-implemented method for training a machine learning system. The training includes: determining, by an encoder of the machine learning system and based on a training input signal, a first intermediate representation characterizing a mean of a latent distribution of a latent space and a second intermediate representation characterizing a variance and/or covariance of the latent distribution; determining, based on the first intermediate representation and the second intermediate representation, a plurality of sigma points with respect to the latent distribution; determining an output signal, wherein the output signal is determined by providing a randomly sampled sigma point of the plurality of sigma points to a decoder of the machine learning system; adapting the machine learning system based on a loss value, wherein the loss value characterizes a difference between the training input signal and the output signal.

Claims10

Dependent claims

A computer-implemented method for training a machine learning system, wherein the machine learning system is configured for anomaly detection and/or sampling a trajectory for a traffic participant and/or sampling of sensor signals and/or for determining a value characterizing a likelihood of an input signal with respect to a training dataset, wherein the training comprises the following steps:

determining, by an encoder of the machine learning system and based on a training input signal, a first intermediate representation characterizing a mean of a latent distribution of a latent space, and a second intermediate representation characterizing a variance and/or covariance of the latent distribution;
determining, based on the first intermediate representation and the second intermediate representation, a plurality of sigma points with respect to the latent distribution;
determining an output signal, wherein the output signal is determined by providing a randomly sampled sigma point of the plurality of sigma points to a decoder of the machine learning system;
and
adapting the machine learning system based on a loss value, wherein the loss value characterizes a difference between the training input signal and the output signal,
wherein the sigma points in the plurality of sigma points are mean-centered symmetric points, comprising the mean characterized by the first intermediate representation;
wherein the plurality of sigma points is determined according to the formulae:
χ0=μ,
χi=μ+√{square root over ((κ+n)Σ)},
χi+n=μ−√{square root over ((κ+n)Σ)},
wherein κ>−n is a predefined real constant, n is a dimensionality of the latent space, μ is the mean, and Σ is the variance and/or covariance;
and
wherein the loss value is determined based on a loss function, wherein the loss function is characterized by the formulae:
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wherein xi is the training input signal, pdata is an empirical distribution including a training dataset, D is the decoder of the machine learning system, and z is a randomly sampled sigma point of the plurality of sigma points
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124 more elements

A computer-implemented method for determining whether an input signal is anomalous or normal, the method comprising the following steps:

obtaining a machine learning system that is configured for anomaly detection and that has been trained by:
determining, by an encoder of the machine learning system and based on a training input signal, a first intermediate representation characterizing a mean of a latent distribution of a latent space, and a second intermediate representation characterizing a variance and/or covariance of the latent distribution,
determining, based on the first intermediate representation and the second intermediate representation, a plurality of sigma points with respect to the latent distribution,
determining an output signal, wherein the output signal is determined by providing a randomly sampled sigma point of the plurality of sigma points to a decoder of the machine learning system, and
adapting the machine learning system based on a loss value, wherein the loss value characterizes a difference between the training input signal and the output signal;
providing the input signal to the encoder of the machine learning system to determine the first intermediate representation;
determining an output signal by providing the first intermediate representation as input to the decoder of the machine learning system;
determining the input signal as anomalous based on a difference between the output signal and the input signal exceeding a predefined threshold and otherwise determining the input signal as normal,
wherein the sigma points in the plurality of sigma points are mean-centered symmetric points, comprising the mean characterized by the first intermediate representation;
wherein the plurality of sigma points is determined according to the formulae:
χ0=μ,
χi=μ+√{square root over ((κ+n)Σ)},
χi+n=μ−√{square root over ((κ+n)Σ)},
wherein κ>−n is a predefined real constant, n is a dimensionality of the latent space, μ is the mean, and Σ is the variance and/or covariance;
and
wherein the loss value is determined based on a loss function, wherein the loss function is characterized by the formulae:
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=
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wherein xi is the training input signal, pdata is an empirical distribution, including a training dataset, D is the decoder of the machine learning system, and z is a randomly sampled sigma point of the plurality of sigma points
{
χ
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.

127 more elements

A computer-implemented method for sampling a trajectory of a traffic participant and/or a sampling sensor signal comprising the following steps:

obtaining a machine learning that has been trained and that is configured for trajectory sampling and/or sampling a sensor signal, the training including:
determining, by an encoder of the machine learning system and based on a training input signal, a first intermediate representation characterizing a mean of a latent distribution of a latent space, and a second intermediate representation characterizing a variance and/or covariance of the latent distribution,
determining, based on the first intermediate representation and the second intermediate representation, a plurality of sigma points with respect to the latent distribution,
determining an output signal, wherein the output signal is determined by providing a randomly sampled sigma point of the plurality of sigma points to a decoder of the machine learning system, and
adapting the machine learning system based on a loss value, wherein the loss value characterizes a difference between the training input signal and the output signal;
randomly drawing a value from the latent space characterized by the machine learning system;
and
determining an output signal characterizing a trajectory by providing the randomly drawn value to the decoder of the machine learning system:
wherein the sigma points in the plurality of sigma points are mean-centered symmetric points, comprising the mean characterized by the first intermediate representation:
wherein the plurality of sigma points is determined according to the formulae:
χ0=μ,
χi=μ+√{square root over ((κ+n)Σ)},
χi+n=μ−√{square root over ((κ+n)Σ)},
wherein κ>−n is a predefined real constant, n is a dimensionality of the latent space, μ is the mean, and Σ is the variance and/or covariance;
and
wherein the loss value is determined based on a loss function, wherein the loss function is characterized by the formulae:
ℒ
U
⁢
A
⁢
E
=
E
x
i
∼
p
d
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∼
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0
2
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n
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K
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L
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μ

2
2
+
tr
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(
∑
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-
n
-
log
⁢
det
∑
,
wherein xi is the training input signal, pdata is an empirical distribution including a training dataset, D is the decoder of the machine learning system, and z is a randomly sampled sigma point of the plurality of sigma points
{
χ
i
(
μ
,
∑
)
}
i
=
0
2
⁢
n
.

127 more elements

A training system comprising at least one processor configured to train a machine learning system, wherein the machine learning system is configured for executed by the least one processor for anomaly detection and/or sampling a trajectory for a traffic participant and/or sampling of sensor signals and/or for determining a value characterizing a likelihood of an input signal with respect to a training dataset, wherein the training system is configured executed by the at least one processor to:

determine, using an encoder of the machine learning system and based on a training input signal, a first intermediate representation characterizing a mean of a latent distribution of a latent space, and a second intermediate representation characterizing a variance and/or covariance of the latent distribution;
determine, based on the first intermediate representation and the second intermediate representation, a plurality of sigma points with respect to the latent distribution;
determine an output signal, wherein the output signal is determined by providing a randomly sampled sigma point of the plurality of sigma points to a decoder of the machine learning system;
and
adapt the machine learning system based on a loss value, wherein the loss value characterizes a difference between the training input signal and the output signal,
wherein the sigma points in the plurality of sigma points are mean-centered symmetric points, comprising the mean characterized by the first intermediate representation;
wherein the plurality of sigma points is determined according to the formulae:
χ0=μ,
χi=μ+√{square root over ((κ+n)Σ)},
χi+n=μ−√{square root over ((κ+n)Σ)},
wherein κ>−n is a predefined real constant, n is a dimensionality of the latent space, μ is the mean, and Σ is the variance and/or covariance;
and
wherein the loss value is determined based on a loss function, wherein the loss function is characterized by the formulae:
ℒ
U
⁢
A
⁢
E
=
E
x
i
∼
p
d
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a
⁢
t
⁢
a
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μ

2
2
+
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∑
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-
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-
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det
∑
,
wherein xi is the training input signal, pdata is an empirical distribution including a training dataset, D is the decoder of the machine learning system, and z is a randomly sampled sigma point of the plurality of sigma points
{
χ
i
(
μ
,
∑
)
}
i
=
0
2
⁢
n
.

124 more elements

A control system comprising at least one processor, which the at least one processor is configured to:

obtain a machine learning system that is configured for anomaly detection and that has been trained by:
determining, by an encoder of the machine learning system and based on a training input signal, a first intermediate representation characterizing a mean of a latent distribution of a latent space, and a second intermediate representation characterizing a variance and/or covariance of the latent distribution,
determining, based on the first intermediate representation and the second intermediate representation, a plurality of sigma points with respect to the latent distribution,
determining an output signal, wherein the output signal is determined by providing a randomly sampled sigma point of the plurality of sigma points to a decoder of the machine learning system, and
adapting the machine learning system based on a loss value, wherein the loss value characterizes a difference between the training input signal and the output signal;
provide an input signal to the encoder of the machine learning system to determine the first intermediate representation;
determine an output signal by providing the first intermediate representation as input to the decoder of the machine learning system;
determine the input signal as anomalous based on a difference between the output signal and the input signal exceeding a predefined threshold and otherwise determining the input signal as normal;
wherein the control system determines a control signal based on the output signal, wherein the control signal is configured to control an actuator and/or a display;
wherein the sigma points in the plurality of sigma points are mean-centered symmetric points, comprising the mean characterized by the first intermediate representation;
wherein the plurality of sigma points is determined according to the formulae:
χ0=μ,
χi=μ+√{square root over ((κ+n)Σ)},
χi+n=μ−√{square root over ((κ+n)Σ)},
wherein κ>−n is a predefined real constant, n is a dimensionality of the latent space, μ is the mean, and Σ is the variance and/or covariance;
and
wherein the loss value is determined based on a loss function, wherein the loss function is characterized by the formulae:
ℒ
U
⁢
A
⁢
E
=
E
x
i
∼
p
d
⁢
a
⁢
t
⁢
a
[
ℒ
R
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E
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C
+
β
·
ℒ
K
⁢
L
]
,
ℒ
R
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E
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C
=

x
i
-
D
⁡
(
z
)
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2
2
,
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∼
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i
(
μ
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}
i
=
0
2
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n
,
ℒ
K
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L
=

μ

2
2
+
tr
⁡
(
∑
)
-
n
-
log
⁢
det
∑
,
wherein xi is the training input signal, pdata is an empirical distribution including a training dataset, D is the decoder of the machine learning system, and z is a randomly sampled sigma point of the plurality of sigma points
{
χ
i
(
μ
,
∑
)
}
i
=
0
2
⁢
n
.

128 more elements

A non-transitory machine-readable storage medium on which is stored a computer program for training a machine learning system, wherein the machine learning system is configured for anomaly detection and/or sampling a trajectory for a traffic participant and/or sampling of sensor signals and/or for determining a value characterizing a likelihood of an input signal with respect to a training dataset, wherein the computer program, when executed by a processor, causing the processor to train the machine learning system by performing the following steps:

determining, by an encoder of the machine learning system and based on a training input signal, a first intermediate representation characterizing a mean of a latent distribution of a latent space, and a second intermediate representation characterizing a variance and/or covariance of the latent distribution;
determining, based on the first intermediate representation and the second intermediate representation, a plurality of sigma points with respect to the latent distribution;
determining an output signal, wherein the output signal is determined by providing a randomly sampled sigma point of the plurality of sigma points to a decoder of the machine learning system;
and
adapting the machine learning system based on a loss value, wherein the loss value characterizes a difference between the training input signal and the output signal,
wherein the sigma points in the plurality of sigma points are mean-centered symmetric points, comprising the mean characterized by the first intermediate representation;
wherein the plurality of sigma points is determined according to the formulae:
χ0=μ,
χi=μ+√{square root over ((κ+n)Σ)},
χi+n=μ−√{square root over ((κ+n)Σ)},
wherein κ>−n is a predefined real constant, n is a dimensionality of the latent space, μ is the mean, and Σ is the variance and/or covariance;
and
wherein the loss value is determined based on a loss function, wherein the loss function is characterized by the formulae:
ℒ
U
⁢
A
⁢
E
=
E
x
i
∼
p
d
⁢
a
⁢
t
⁢
a
[
ℒ
R
⁢
E
⁢
C
+
β
·
ℒ
K
⁢
L
]
,
ℒ
R
⁢
E
⁢
C
=

x
i
-
D
⁡
(
z
)

2
2
,
z
∼
{
χ
i
(
μ
,
∑
)
}
i
=
0
2
⁢
n
,
ℒ
K
⁢
L
=

μ

2
2
+
tr
⁡
(
∑
)
-
n
-
log
⁢
det
∑
,
wherein xi is the training input signal, pdata is an empirical distribution including a training dataset, D is the decoder of the machine learning system, and z is a randomly sampled sigma point of the plurality of sigma points
{
χ
i
(
μ
,
∑
)
}
i
=
0
2
⁢
n
.

124 more elements

Dependencies are inferred from the claim text.

Facts

Application: 18/465,627Filed 12 Sep 2023
Priority: EP 2219696321 Sep 2022
Published: US 2024/0095595 A121 Mar 2024
Granted: 11 Aug 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification9 codes

G06N 3/0455
Combinations of networks: auto-encoder networks; Encoder-decoder networks
G06N 20/00
Machine learning
G05B 19/045
Program control other than numerical control, i.e. in sequence controllers or logic controllers: using logic state machines, consisting only of a…
G06N 3/0475
Architecture, e.g. interconnection topology: generative networks