International Business Machines Corporation

Teng Jiao Li

Xiao Lin Sun

Zhong Fang Yuan

Tong Liu

Tian Ji Yang

Ya Juan Dang

Wen Jie Hao

Provided are techniques for identifying and correcting model focus drift during model training. A model is trained using an original dataset with data, and the is classified into clusters. A representation is assigned to each of the clusters. A first visualization dashboard with visualizations is generated, where each visualization represents a first data distribution of an associated cluster using the representation assigned to that cluster. The model is fine-tuned using a fine-tune dataset. A second visualization dashboard is generated by updating each visualization, where each visualization represents a second data distribution of the associated cluster. It is determined that a cluster of the clusters has focus drift based on changes between the first data distribution and the second data distribution. The focus drift is corrected by: adding the data of the cluster to the fine-tune dataset to form a combined dataset and fine-tuning the model using the combined dataset.

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BeiJing, China

Teng Jiao Li