Deep Learning, which can be treated as the most significant breakthrough in the past 10 years in the field of pattern recognition and machine learning, has greatly affected the methodology of related fields like computer vision and achieved terrific progress in both academy and industry. It can be seen as a resolution to change the whole pattern recognition system. It achieved an end-to-end pattern recognition, merging the previous steps of pre-processing, feature extraction, classifier design and post-processing. It is expected that the development of deep learning theories and applications would further influence the field of pattern recognition.

The major goal of this workshop is to provide a platform for researchers or graduate students around the world to report or exchange their progresses on deep learning for pattern recognition.

Scope and Topics

  • Deep learning architectures for pattern recognition
  • Optimization for deep learning
  • Sparse coding in deep learning
  • Transfer learning for deep learning
  • Deep learning for feature representation
  • Deep learning for facial analysis
  • Deep learning for object recognition
  • Deep learning for scene understanding
  • Deep learning for document analysis
  • Deep learning for dimension reduction
  • Deep learning for activity recognition
  • Deep learning for semantic segmentation
  • Deep learning for generative modeling
  • Deep learning for biometrics
  • Multi-modal deep learning
  • Performance evaluation of deep learning algorithms