Continual Unsupervised Representation Learning (1910.14481v1)

Abstract: Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially. Prior art in the field has largely considered supervised or reinforcement learning tasks, and often assumes full knowledge of task labels and boundaries. In this work, we propose an approach (CURL) to tackle a more general problem that we will refer to as unsupervised continual learning. The focus is on learning representations without any knowledge about task identity, and we explore scenarios when there are abrupt changes between tasks, smooth transitions from one task to another, or even when the data is shuffled. The proposed approach performs task inference directly within the model, is able to dynamically expand to capture new concepts over its lifetime, and incorporates additional rehearsal-based techniques to deal with catastrophic forgetting. We demonstrate the efficacy of CURL in an unsupervised learning setting with MNIST and Omniglot, where the lack of labels ensures no information is leaked about the task. Further, we demonstrate strong performance compared to prior art in an i.i.d setting, or when adapting the technique to supervised tasks such as incremental class learning.

• The paper introduces CURL, a framework that uses model-internal task inference to effectively address catastrophic forgetting in unsupervised continual learning.
• The methodology employs a latent mixture-of-Gaussians and dynamic capacity expansion to seamlessly adapt to new data without explicit task labels.
• Empirical evaluations on MNIST and Omniglot demonstrate CURL's robust performance in handling abrupt and gradual task transitions while outperforming benchmarks.

Continual Unsupervised Representation Learning: A Focus on CURL

The paper "Continual Unsupervised Representation Learning" presents CURL, a novel approach addressing the challenges associated with unsupervised continual learning. This problem context is distinct in its complexity, as it necessitates learning representations without pre-established task identities or boundaries. Traditional continual learning methodologies primarily address supervised or reinforcement learning environments, which typically benefit from task labels. In contrast, the current paper acknowledges and tackles the unsupervised domain challenges, including scenarios with abrupt task changes, smooth task transitions, or completely shuffled data.

CURL leverages a unique model-internal task inference strategy that allows for dynamic learning and adaptation over a system's lifespan. By doing so, it effectively introduces mechanisms to mitigate catastrophic forgetting, a critical barrier in sequential learning scenarios. The efficacy of CURL is demonstrated through empirical evaluations involving unsupervised learning on MNIST and Omniglot datasets. In these settings, the lack of labels enforces a purely unsupervised methodology, with promising outcomes in maintaining representation integrity.

Model Specifics and Methodology

The CURL framework employs a generative model where a categorical task variable manifests as a latent mixture-of-Gaussians, subsequently decoded to generate the input. The inference process accounts for the absence of task labels by introducing a variational approximation that separates task inference from latent representation learning.

In terms of dynamic model behavior, CURL can expand its capacity as needed—a critical feature for learning new concepts or variations in data. The mechanism leveraged for this involves monitoring a buffer of poorly modeled samples; upon reaching a predefined threshold, the model initializes new components to address the newly realized complexity in incoming data.

Furthermore, CURL introduces mixture generative replay (MGR) as a countermeasure against forgetting. This technique reuses generated data from previous model states to enhance learning consistency across new tasks. MGR ensures that each task or concept retains a generative replay trace, harmonizing with dynamic task environments.

Empirical Evaluations

The proposed framework was extensively tested using settings that simulate continual learning with task ambiguities, such as the sequential and continuous drift scenarios on MNIST and Omniglot. These tests highlighted CURL's ability to learn class-discriminative representations over time. Results demonstrated the model's capacity to maintain accuracy while reducing interference between old and new tasks. Performance against existing benchmarks in supervised and unsupervised settings indicated the framework's robustness and adaptability. In the supervised domain, CURL was competently modulated to handle incremental class and task learning, achieving competitive results against established methods like iCARL.

Theoretical and Practical Implications

The implications of CURL extend across both theoretical and practical dimensions. Theoretically, the paper advances understanding of mixture model dynamics in unsupervised settings, exploring how task-agnostic frameworks can self-regulate learning pathways. Practically, CURL’s insights are crucial for deploying learning systems devoid of explicit task guidance, which is typical in real-world applications. Its capacity to leverage unsupervised data streams robustly offers significant utility in domains like robotics and autonomous systems where clear task definitions are often unavailable.

Conclusion and Future Prospects

CURL's approach marks a step forward in unsupervised continual learning by addressing intricate challenges associated with dynamic, non-stationary environments and unlabeled data. Future research avenues could further explore extending CURL to reinforcement learning contexts or optimizing computational efficiency through refined expansion and replay strategies. The exploration of hybrid models that incorporate both generative replay and other anti-forgetting mechanisms might also bolster CURL's versatility across diverse application domains.