Continual Learning for Deep Dense Prediction

Transferring a deep learning model from old tasks to a new one is known to suffer from the catastrophic forgetting effects. Such forgetting mechanism is problematic as it does not allow us to accumulate knowledge sequentially and requires retaining and retraining on all the training data. Existing techniques for mitigating the abrupt performance degradation on previously trained tasks are mainly studied in the context of image classification. In this work, we present a simple method to alleviate catastrophic forgetting for pixel-wise dense labeling problems. We build upon the regularization technique using knowledge distillation to minimize the discrepancy between the posterior distribution of pixel class labels for old tasks predicted from 1) the original and 2) the updated networks. This technique, however, might fail in circumstances where the source and target distribution differ significantly. To handle the above scenario, we further propose an improvement to the distillation based approach by adding adaptive l2-regularization depending upon the per-parameter importance to the older tasks. We train our model on FCN8s, but our training can be generalized to stronger models like DeepLab, PSPNet, etc. Through extensive evaluation and comparisons, we show that our technique can incrementally train dense prediction models for novel object classes, different visual domains, and different visual tasks.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2018

Author dc:creator

• Lokegaonkar, Sanket Avinash

Chairs dc:contributor.committeechair

• Ramakrishnan, Naren
• Huang, Jia-Bin

Committee member dc:contributor.committeemember

• Huang, Bert

• Computer Vision
• Continual Learning
• Image Segmentation
• Dense Prediction

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:16094

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/83513