SPICE: Synergy and Partial Information Based Curriculum Evolution

2026-06-15

AI Summary

This work addresses the challenge of dynamically varying sample complexity in multimodal learning, which existing curriculum learning approaches struggle to accommodate as models evolve. It introduces, for the first time, Partial Information Decomposition (PID) theory into multimodal curriculum learning, proposing a progressive curriculum framework that decomposes multimodal interactions into redundant, unique, and synergistic information components. This decomposition enables a dynamic characterization of sample complexity, which in turn drives an adaptive scheduling of training samples aligned with the model’s learning progress. The resulting approach yields an interpretable, training-aware sample selection mechanism that significantly outperforms conventional training strategies and state-of-the-art baselines across multiple multimodal benchmarks, demonstrating both its effectiveness and generalizability.

Abstract

Multimodal learning exploits complementary information across heterogeneous modalities. The informativeness of each modality can vary widely across samples and training stages. Existing multimodal curriculum learning strategies often assume that the relative complexity of samples remains unchanged throughout training and therefore cannot adapt to model evolution. We propose SPICE (Synergy and Partial Information based Curriculum Evolution), a novel progressive curriculum framework for multimodal interaction learning. Guided by Partial Information Decomposition (PID) theory, our approach decomposes multimodal interactions into redundant, unique, and synergistic information components, enabling an interpretable and dynamic characterization of sample complexity. Building on this decomposition, we design a progressive curriculum that evolves throughout training, allowing the model to transition from learning shared cross-modal cues to modality-specific patterns and, finally, to complex synergistic interactions. Adapting to model evolution, sample ordering is refined in real-time using PID information estimates derived from unimodal and multimodal predictions. Experiments across multiple multimodal benchmarks demonstrate consistent improvements over conventional training and state-of-the-art baselines, highlighting the effectiveness of PID information decomposition and adaptive sample ordering for multimodal curriculum learning.

Problem

• multimodal learning
• curriculum learning
• sample complexity
• model evolution
• information decomposition

Innovation

• Partial Information Decomposition
• Multimodal Curriculum Learning
• Synergistic Interaction
• Adaptive Sample Ordering
• Progressive Curriculum