IEEE Transactions on Automation Science and Engineering, 2022
Curriculum learning and self-paced learning are the training strategies that
gradually feed the samples from easy to more complex. They have captivated
increasing attention due to their excellent performance in robotic vision. Most
recent works focus on designing curricula based on difficulty levels in input
samples or smoothing the feature maps. However, smoothing labels to control the
learning utility in a curriculum manner is still unexplored. In this work, we
design a paced curriculum by label smoothing (P-CBLS) using paced learning with
uniform label smoothing (ULS) for classification tasks and fuse uniform and
spatially varying label smoothing (SVLS) for semantic segmentation tasks in a
curriculum manner. In ULS and SVLS, a bigger smoothing factor value enforces a
heavy smoothing penalty in the true label and limits learning less information.
Therefore, we design the curriculum by label smoothing (CBLS). We set a bigger
smoothing value at the beginning of training and gradually decreased it to zero
to control the model learning utility from lower to higher. We also designed a
confidence-aware pacing function and combined it with our CBLS to investigate
the benefits of various curricula. The proposed techniques are validated on
four robotic surgery datasets of multi-class, multi-label classification,
captioning, and segmentation tasks. We also investigate the robustness of our
method by corrupting validation data into different severity levels. Our
extensive analysis shows that the proposed method improves prediction accuracy
and robustness.
Mengya Xu, Mobarakol Islam, Ben Glocker, Hongliang Ren
2022-12-22
