Adobe, Inc.

Manoj Ghuhan Arivazhagan

Vishwa Vinay

Atanu Ranjan Sinha

Sumit Shekhar

Sunav Choudhary

Paridhi Maheshwari

Tripti Shukla

Sapthotharan Krishnan Nair

Abhilasha Sancheti

Divya Kothandaraman

Sarthak Chakraborty

Shivi Gupta

Atharv Tyagi

Yuvraj

Atharva Anand Joshi

Systems and methods for machine learning are described. The systems and methods include receiving target training data including a training image and ground truth label data for the training image, generating source network features for the training image using a source network trained on source training data, generating target network features for the training image using a target network, generating at least one attention map for training the target network based on the source network features and the target network features using a guided attention transfer network, and updating parameters of the target network based on the attention map and the ground truth label data.

System and methods for active domain adaptation

A system includes a representation generator subsystem configured to execute a user representation model and a task prediction model to generate a user representation for a user. The user representation model receives user event sequence data comprises a sequence of user interactions with the system. The task prediction model is configured to train the user representation model. The user representation includes a vector of a predetermined size that represents the user event sequence data and is generated by applying the trained user representation model to the user event sequence data. A storage requirement of the user representation is less than a storage space requirement of the user event sequence data. The system includes a data store configured for storing the user representation in a user profile associated with the user.

Generating concise and common user representations for edge systems from event sequence data stored on hub systems

The disclosed techniques include at least one computer-implemented method performed by a system. The system can receive a textual query and process query features of the textual query to identify a color profile indicative of a color intent of the query. The system can identify candidate images that at least partially match the desired content and color intent of the query. The system can further order candidate images based in part on a similarity of a candidate color profile for each candidate image with the identified color profile of the query, and output image data indicative of the ordered set of candidate images.

Techniques for identifying color profiles for textual queries

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Tamil Nadu, India

Manoj Ghuhan Arivazhagan