Techniques for inter-operation of a two-sided artificial intelligence model using an adapter
AssigneeLenovo (United States) Inc.
InventorsMahdi Barzegar Khalilsarai, Vahid Pourahmadi
Life of this patent
Estimating the term…
Abstract
Various aspects of the present disclosure relate to techniques for inter-operation of a two-sided artificial intelligence model using an adapter. An apparatus is configured to receive an indication of an anomaly in data output by the encoder, the encoder associated with a decoder at a receiver node, receive a training data set for training the adapter network in response to the indication of the anomaly, train the adapter network using the training data set, process the data output from the encoder using the adapter network prior to transmitting the data to the decoder, and transmit the processed data to the decoder.
Claims20
A transmitter node for wireless communication, comprising:
- an encoder;
- an adapter network communicatively coupled to the encoder;
- at least one memory;
- and
- at least one processor coupled with the at least one memory and configured to cause the transmitter node to:
- receive an indication of an anomaly in data output by the encoder, the encoder associated with a decoder at a receiver node;
- receive a training data set for training the adapter network in response to the indication of the anomaly, the adapter network comprising a machine learning model that is configured to correct anomalies in the data output by the encoder;
- train the adapter network using the training data set;
- process the data output from the encoder using the adapter network prior to transmitting the data to the decoder;
- and
- transmit the processed data to the decoder.
A method of a transmitter node, comprising:
- receiving an indication of an anomaly in data output by an encoder, the encoder associated with a decoder at a receiver node;
- receiving a training data set for training an adapter network in response to the indication of the anomaly, the adapter network comprising a machine learning model that is communicatively coupled to the encoder and is configured to correct anomalies in the data output by the encoder;
- training the adapter network using the training data set;
- processing the data output from the encoder using the adapter network prior to transmitting the data to the decoder;
- and
- transmitting the processed data to the decoder.
A receiver node for wireless communication, comprising:
- a decoder;
- an adapter network communicatively coupled to the decoder;
- at least one memory;
- and
- at least one processor coupled with the at least one memory and configured to cause the receiver node to:
- receive data output by an encoder of a transmitter node, the encoder associated with the decoder;
- determine an indication of an anomaly in the data output by the encoder;
- receive a training data set for training the adapter network in response to the indication of the anomaly, the adapter network comprising a machine learning model that is configured to correct anomalies in the data output by the encoder;
- train the adapter network using the training data set;
- process the data output by the encoder using the adapter network;
- and
- decode, by the decoder, output from the adapter network.
A method of a receiver node, comprising:
- receiving data output by an encoder of a transmitter node, the encoder associated with a decoder of the receiver node;
- determine an indication of an anomaly in the data output by the encoder;
- receive a training data set for training an adapter network in response to the indication of the anomaly, the adapter network comprising a machine learning model that is configured to correct anomalies in the data output by the encoder;
- train the adapter network using the training data set;
- process the data output by the encoder using the adapter network;
- and
- decode, by the decoder, output from the adapter network.
Dependencies are inferred from the claim text.