Abstract: A method for scoring training data samples according to an ability to preserve latent decision boundaries for previously observed classes while promoting learning from an input batch of new images from an online data stream, comprising: receiving the input batch of the new images from the online data stream, performing a memory retrieval process that retrieves data to be learned along with a new set of data from the memory to retain the previously learned knowledge, and performing a memory update process that selects and exchanges a small set of data to be saved in the memory in the memory update process. In addition, the method performs data valuation based on KNN-SV for both the memory retrieval and memory update processes to perform strategic and intuitive data selection based on the properties of KNN-SV.

Abstract: A task-oriented dialogue method may be performed by a computing system including a memory and a processor using a dialogue model. The task-oriented dialogue method includes: generating a dialogue graph which models at least one conditional relationship for a dialogue dataset; receiving a user dialogue input; sampling a plurality of dialogue act groups for responding to the user dialogue input by using a pre-trained dialogue model; adjusting the plurality of dialogue act groups based on the dialogue graph; and selecting any one dialogue act group which satisfies a predetermined condition among the plurality of dialogue act groups.

Abstract: A method for automatically performing a task may perform a task based on a user dialogue input by a computing system including a memory and a processor. The method may include: receiving a user dialogue input; determining a type of a task requested by a user by analyzing the user dialogue input; obtaining task context data required to perform the task based on the user dialogue input; and performing the task of which type is determined based on the task context data.

Abstract: A task performing method may perform a task based on a context of a task-oriented dialogue through a dialogue model by a computing system including a memory and a processor. The task performing method may include: receiving a user dialogue input; determining and providing a response dialogue act to the user dialogue input based on a dialogue graph; determining the context of the task-oriented dialogue by analyzing data of a series of task-oriented dialogues including the user dialogue input and the response dialogue act; determining the type of task requested by a user based on the context of the task-oriented dialogue; and performing the task of which type is determined.

Abstract: A vision inspection device includes: a memory including a buffer; and a processor configured to: acquire a plurality of divided images by dividing a captured product image into a plurality of pieces and a new data set including new normal product type data and new defective type data corresponding to the plurality of divided images; sample at least one buffer data set among a plurality of buffer data sets stored in the buffer; generate a mini batch by combining the sampled buffer data set with the new data set; and determine whether to store the new data set in the buffer by using a soft nearest neighbor loss (SNNL) value of the new data set constituting the mini batch, and a cumulative average SNNL value of each of the buffer data sets constituting the mini batch.

Abstract: A method for scoring training data samples according to an ability to preserve latent decision boundaries for previously observed classes while promoting learning from an input batch of new images from an online data stream, comprising: receiving the input batch of the new images from the online data stream, performing a memory retrieval process that retrieves data to be learned along with a new set of data from the memory to retain the previously learned knowledge, and performing a memory update process that selects and exchanges a small set of data to be saved in the memory in the memory update process. In addition, the method performs data valuation based on KNN-SV for both the memory retrieval and memory update processes to perform strategic and intuitive data selection based on the properties of KNN-SV.