SPCNet: Deep Self-Paced Curriculum Network Incorporated With Inductive Bias.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Massively parameterized Convolutional Neural Networks (CNNs) struggle with generalizability and reliability on complex data due to poor local optima and noise memorization.
• Self-Paced Curriculum Learning (SPCL) offers a solution by mimicking human learning progression but faces challenges in deep networks.
• Existing SPCL methods often rely on prior knowledge via regularizers and involve tedious iterative training.

Purpose of the Study:

• To develop an efficient and general-purpose deep SPCL framework that integrates seamlessly with neural networks.
• To address the limitations of existing SPCL approaches in handling deep networks.
• To improve the generalizability and reliability of CNNs on complex real-world data.

Main Methods:

• Proposed a novel Self-Paced Curriculum Network (SPCNet) that incorporates an attention mechanism for adaptive instance contribution.
• Integrated implicit regularizer preferences directly into the network structure via inductive bias.
• Enabled simultaneous online difficulty estimation, adaptive sample selection, and end-to-end model updating.

Main Results:

• The proposed SPCNet facilitates the collaboration of SPCL with deep networks efficiently.
• Experiments on image and scene classification tasks showed superior performance compared to state-of-the-art methods.
• The approach demonstrated enhanced generalizability and reliability of CNNs.

Conclusions:

• The novel SPCNet paradigm effectively overcomes limitations of traditional SPCL in deep learning.
• Attention mechanisms and integrated inductive biases provide an efficient framework for deep SPCL.
• SPCNet offers a promising direction for improving deep neural network performance on complex datasets.