Abstract: In accordance with a first aspect, an improved compression efficiency is achieved by letting a block-wise picture codec support a set of intra-prediction modes according to which the intra-prediction signal for a current block of a picture is determined by applying a set of neighboring samples of the current block onto a neural network. A second aspect of the present application is that, additionally or alternatively to the spending of neural network-based intra-prediction modes, the mode selection may be rendered more effective by the usage of a neural network dedicated to determine a rank or a probability value for each of the set of intra-prediction modes by applying a set of neighboring samples thereonto with the rank or probability value being used for the selection of one intra-prediction mode out of the plurality of intra-prediction modes including or coinciding with the set of intra-prediction modes.

Abstract: Disclosed is a decoder for decoding parameters of a neural network, configured to obtain a plurality of neural network parameters of the neural network on the basis of an encoded bitstream, to obtain, e.g. to receive; e.g. to extract from an encoded bitstream, a node information describing a node of a parameter update tree, wherein the node information has a parent node identifier, which is, for example, a unique parent node identifier, for example an integer number, a string, and/or a cryptographic hash, and wherein the node information has a parameter update information, e.g. one or more update instructions, for example a difference signal between initial neural network parameters and a newer version thereof, e.g. corresponding to a child node of the update tree, and to derive one or more neural network parameters using parameter information of a parent node identified by the parent node identifier and using the parameter update information.

Abstract: Embodiments according to the invention comprise an apparatus for decoding neural network parameters, which define a neural network. The apparatus may, optionally, be configured to obtain, e.g. to decode, parameters of a base model, e.g. NB, of the neural network which define one or more layers, e.g. base layers, of the neural network. Furthermore, the apparatus is configured to decode an update model, e.g. NU1 to NUK, which defines a modification of one or more layers, e.g. base layers, of the neural network, and the apparatus is configured modify parameters of a base model of the neural network using the update model, in order to obtain an updated model, e.g. designated as “new model” comprising new model layers LNkj. Moreover, the apparatus is configured to evaluate a skip information, e.g. a skip_row_flag and/or a skip_column_flag, indicating whether a sequence, e.g. a row, or a column or a block, of parameters of the update model is zero or not.

Abstract: In accordance with a first aspect, an improved compression efficiency is achieved by letting a block-wise picture codec support a set of intra-prediction modes according to which the intra-prediction signal for a current block of a picture is determined by applying a set of neighboring samples of the current block onto a neural network. A second aspect of the present application is that, additionally or alternatively to the spending of neural network-based intra-prediction modes, the mode selection may be rendered more effective by the usage of a neural network dedicated to determine a rank or a probability value for each of the set of intra-prediction modes by applying a set of neighboring samples thereonto with the rank or probability value being used for the selection of one intra-prediction mode out of the plurality of intra-prediction modes including or coinciding with the set of intra-prediction modes.

Abstract: An apparatus for block-wise decoding a picture from a data stream and/or encoding a picture into a data stream, the apparatus supporting at least one intra-prediction mode according to which the intra-prediction signal for a block of a predetermined size of the picture is determined by applying a first template of samples which neighbours the current block onto a neural network. The apparatus may be configured, for a current block differing from the predetermined size, to: resample a second template of samples neighboring the current block, so as to conform with the first template so as to obtain a resampled template ; apply the resampled template of samples onto the neural network so as to obtain a preliminary intra-prediction signal; and resample the preliminary intra-prediction signal so as to conform with the current block so as to obtain the intra-prediction signal for the current block.

Abstract: Apparatus for generating a NN representation, configured to quantize an NN parameter onto a quantized value by determining a quantization parameter and a quantization value for the NN parameter so that from the quantization parameter, there is derivable a multiplier and a bit shift number. Additionally, the determining of the quantization parameter and the quantization value for the NN parameter is performed so that the quantized value of the NN parameter corresponds to a product between the quantization value and a factor, which depends on the multiplier, bit-shifted by a number of bits which depends on the bit shift number.

Abstract: An apparatus for block-wise decoding a picture from a data stream and/or encoding a picture into a data stream, the apparatus supporting at least one intra-prediction mode according to which the intra-prediction signal for a block of a predetermined size of the picture is determined by applying a first template of samples which neighbours the current block onto a neural network. The apparatus may be configured, for a current block differing from the predetermined size, to: resample a second template of samples neighboring the current block, so as to conform with the first template so as to obtain a resampled template; apply the resampled template of samples onto the neural network so as to obtain a preliminary intra-prediction signal; and resample the preliminary intra-prediction signal so as to conform with the current block so as to obtain the intra-prediction signal for the current block.

Abstract: In accordance with a first aspect, an improved compression efficiency is achieved by letting a block-wise picture codec support a set of intra-prediction modes according to which the intra-prediction signal for a current block of a picture is determined by applying a set of neighboring samples of the current block onto a neural network. A second aspect of the present application is that, additionally or alternatively to the spending of neural network-based intra-prediction modes, the mode selection may be rendered more effective by the usage of a neural network dedicated to determine a rank or a probability value for each of the set of intra-prediction modes by applying a set of neighboring samples thereonto with the rank or probability value being used for the selection of one intra-prediction mode out of the plurality of intra-prediction modes including or coinciding with the set of intra-prediction modes.

Abstract: Data stream having a representation of a neural network encoded thereinto, the data stream including serialization parameter indicating a coding order at which neural network parameters, which define neuron interconnections of the neural network, are encoded into the data stream.

Abstract: In accordance with a first aspect, an improved compression efficiency is achieved by letting a block-wise picture codec support a set of intra-prediction modes according to which the intra-prediction signal for a current block of a picture is determined by applying a set of neighboring samples of the current block onto a neural network. A second aspect of the present application is that, additionally or alternatively to the spending of neural network-based intra-prediction modes, the mode selection may be rendered more effective by the usage of a neural network dedicated to determine a rank or a probability value for each of the set of intra-prediction modes by applying a set of neighboring samples thereonto with the rank or probability value being used for the selection of one intra-prediction mode out of the plurality of intra-prediction modes including or coinciding with the set of intra-prediction modes.

Abstract: Pruning and/or quantizing a machine learning predictor or, in other words, a machine learning model such as a neural network is rendered more efficient if the pruning and/or quantizing is performed using relevance scores which are determined for portions of the machine learning predictor on the basis of an activation of the portions of the machine learning predictor manifesting itself in one or more inferences performed by the machine learning (ML) predictor.

Abstract: A concept for Federated Learning which is more efficient and/or robust is presented. Beyond this, concepts for specifying clients and/or measuring training data similarities in a manner more suitable for being applied in Federated Learning environments, are described.

Abstract: An encoder for encoding weight parameters of a neural network is configured to obtain a plurality of weight parameters of the neural network, to encode the weight parameters of the neural network using a context-dependent arithmetic coding, to select a context for an encoding of a weight parameter, or for an encoding of a syntax element of a number representation of the weight parameter, in dependence on one or more previously encoded weight parameters and/or in dependence on one or more previously encoded syntax elements of a number representation of one or more weight parameters, and to encode the weight parameter, or a syntax element of the weight parameter, using the selected context. Corresponding decoder, quantizer, methods and computer programs are also described.

Abstract: The present application is concerned with several aspects of improving the efficiency of distributed learning.

Abstract: An apparatus for block-wise decoding a picture from a data stream and/or encoding a picture into a data stream, the apparatus supporting at least one intra-prediction mode according to which the intra-prediction signal for a block of a predetermined size of the picture is determined by applying a first template of samples which neighbours the current block onto a neural network. The apparatus may be configured, for a current block differing from the predetermined size, to: resample a second template of samples neighboring the current block, so as to conform with the first template so as to obtain a resampled template; apply the resampled template of samples onto the neural network so as to obtain a preliminary intra-prediction signal; and resample the preliminary intra-prediction signal so as to conform with the current block so as to obtain the intra-prediction signal for the current block.

Abstract: In accordance with a first aspect, an improved compression efficiency is achieved by letting a block-wise picture codec support a set of intra-prediction modes according to which the intra-prediction signal for a current block of a picture is determined by applying a set of neighboring samples of the current block onto a neural network. A second aspect of the present application is that, additionally or alternatively to the spending of neural network-based intra-prediction modes, the mode selection may be rendered more effective by the usage of a neural network dedicated to determine a rank or a probability value for each of the set of intra-prediction modes by applying a set of neighboring samples thereonto with the rank or probability value being used for the selection of one intra-prediction mode out of the plurality of intra-prediction modes including or coinciding with the set of intra-prediction modes.

Abstract: The task of relevance score assignment to a set of items onto which an artificial neural network is applied is obtained by redistributing an initial relevance score derived from the network output, onto the set of items by reversely propagating the initial relevance score through the artificial neural network so as to obtain a relevance score for each item. In particular, this reverse propagation is applicable to a broader set of artificial neural networks and/or at lower computational efforts by performing same in a manner so that for each neuron, preliminarily redistributed relevance scores of a set of downstream neighbor neurons of the respective neuron are distributed on a set of upstream neighbor neurons of the respective neuron according to a distribution function.