Plasticity-Stability Preserving Multi-Task Learning for Remote Sensing Image Retrieval

Gencer Sumbül

doi:10.1109/TGRS.2022.3160097

Plasticity-Stability Preserving Multi-Task Learning for Remote Sensing Image Retrieval

2022-01-01

9

OA

AI

DOI：10.1109/TGRS.2022.3160097

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Deep learning-based multi-task learning (MTL) methods have recently attracted attention for content-based image retrieval (CBIR) applications in remote sensing (RS). For a given set of tasks (e.g., scene classification, semantic segmentation, and image reconstruction), existing MTL methods employ a joint optimization algorithm on the direct aggregation of task-specific loss functions. Such an approach may provide limited CBIR performance when: 1) tasks compete or even distract each other; 2) one of the tasks dominates the whole learning procedure; or 3) characterization of each task is underperformed compared to single-task learning. This is mainly due to the lack of: 1) plasticity condition (which is associated with sensitivity to new information) or 2) stability condition (which is associated with protection from radical disruptions by new information) of the whole learning procedure. To avoid this issue, as a first time, we propose a novel plasticity-stability preserving MTL (PLASTA-MTL) approach to ensure the plasticity and the stability conditions of the whole learning procedure independently of the number and type of tasks. This is achieved by defining two novel loss functions. The first loss function is the plasticity preserving loss (PPL) function that aims to enforce the global image representation space to be sensitive to new information learned with each task. This is achieved by minimizing the difference of gradient magnitudes for the global representation and task-specific embedding spaces. The second loss function is the stability preserving loss (SPL) function that aims to protect the global representation space radically disrupted by a new task. This is achieved by minimizing the angular distances between the task gradients over global representation space. To effectively employ the proposed loss functions, we also introduce a novel sequential optimization algorithm. Experimental results show the effectiveness of the proposed approach compared to the state-of-the-art MTL methods in the context of CBIR.

Keyword:

Task analysis

Image representation

Optimization

Image reconstruction

Semantics

Image analysis

Training

Image retrieval

multi-task learning (MTL)

remote sensing (RS)

representation learning

Plasticity-Stability Preserving Multi-Task Learning for Remote Sensing Image Retrieval

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