Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams (2009.00919v4)

Abstract: Attaining prototypical features to represent class distributions is well established in representation learning. However, learning prototypes online from streaming data proves a challenging endeavor as they rapidly become outdated, caused by an ever-changing parameter space during the learning process. Additionally, continual learning does not assume the data stream to be stationary, typically resulting in catastrophic forgetting of previous knowledge. As a first, we introduce a system addressing both problems, where prototypes evolve continually in a shared latent space, enabling learning and prediction at any point in time. In contrast to the major body of work in continual learning, data streams are processed in an online fashion, without additional task-information, and an efficient memory scheme provides robustness to imbalanced data streams. Besides nearest neighbor based prediction, learning is facilitated by a novel objective function, encouraging cluster density about the class prototype and increased inter-class variance. Furthermore, the latent space quality is elevated by pseudo-prototypes in each batch, constituted by replay of exemplars from memory. As an additional contribution, we generalize the existing paradigms in continual learning to incorporate data incremental learning from data streams by formalizing a two-agent learner-evaluator framework. We obtain state-of-the-art performance by a significant margin on eight benchmarks, including three highly imbalanced data streams.

• The paper introduces Continual Prototype Evolution (CoPE), a novel method allowing class prototypes to evolve in a shared latent space for online learning from non-stationary data streams.
• Key innovations include a new objective function for better class separation, the use of pseudo-prototypes to enhance latent space quality, and a general learner-evaluator framework.
• CoPE achieves state-of-the-art performance on eight benchmarks, significantly outperforming existing methods in handling catastrophic forgetting and data imbalance for practical applications.

Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams

The paper "Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams" introduces a novel methodology for tackling the challenges associated with continual learning in dynamic environments. The central problem addressed by the authors is the outdated nature of prototypes during online learning from non-stationary data streams, compounded by catastrophic forgetting as data streams shift. The solution proposed is a system named Continual Prototype Evolution (CoPE), which innovatively allows prototypes to evolve within a shared latent space, facilitating continuous learning and prediction.

Main Contributions

The authors introduce several key innovations in continual learning:

1. Evolving Prototypes: CoPE proposes a mechanism where class prototypes progress continually through an evolving representation space. This approach shifts the focus of catastrophic forgetting from the full network parameter space to a lower-dimensional latent space.
2. Novel Objective Function: The learning process is driven by a new objective function that enhances intra-class cluster density around the class prototype while promoting increased inter-class variance, thereby improving the separation between classes in the latent space.
3. Pseudo-Prototypes: CoPE introduces pseudo-prototypes within each processing batch to further augment the latent space quality, effectively utilizing memory replay from exemplars.
4. Learner-Evaluator Framework: The authors generalize the continual learning paradigms by formalizing a two-agent learner-evaluator framework, facilitating data incremental learning without task-specific information. This framework distinctly separates the optimization and evaluation processes of the continual learning system.

Experimental Evaluation

The researchers demonstrate state-of-the-art performance across eight benchmarks, including three highly imbalanced data streams. CoPE significantly outperforms existing methods, such as GEM, iCaRL, Reservoir, and MIR, by leveraging its evolving prototype strategy and robust memory management. The balanced replay scheme ensures that all classes are fairly represented, mitigating performance degradation due to class imbalance.

Future Directions and Implications

The implications of this work are far-reaching for practical applications of AI in environments with dynamic data streams, such as autonomous vehicles and robotics. The proposed learner-evaluator framework opens new avenues for exploring continual learning paradigms, especially in scenarios devoid of explicit task delineation.

Future research could extend this work by exploring more complex and real-world data streams, optimizing the balance between memory usage and computational efficiency, and refining the learner-evaluator framework for real-time adaptability. Furthermore, integrating CoPE with other AI systems could enhance robustness in domains such as sensor data interpretation, video analysis, and more.

In conclusion, the paper presents a substantial advancement in continual learning methodologies by proposing a system that effectively addresses prototype stagnation and catastrophic forgetting, setting a new standard for learning adaptability in non-stationary environments.