Meta-Learning Representations for Continual Learning (1905.12588v2)

Abstract: A continual learning agent should be able to build on top of existing knowledge to learn on new data quickly while minimizing forgetting. Current intelligent systems based on neural network function approximators arguably do the opposite---they are highly prone to forgetting and rarely trained to facilitate future learning. One reason for this poor behavior is that they learn from a representation that is not explicitly trained for these two goals. In this paper, we propose OML, an objective that directly minimizes catastrophic interference by learning representations that accelerate future learning and are robust to forgetting under online updates in continual learning. We show that it is possible to learn naturally sparse representations that are more effective for online updating. Moreover, our algorithm is complementary to existing continual learning strategies, such as MER and GEM. Finally, we demonstrate that a basic online updating strategy on representations learned by OML is competitive with rehearsal based methods for continual learning. We release an implementation of our method at https://github.com/khurramjaved96/mrcl .

• The paper introduces the Online-aware Meta-Learning (OML) objective that robustly trains neural representations to resist catastrophic forgetting.
• It separates representation learning from prediction tasks by using a meta-objective to maintain performance across sequential tasks.
• Experimental results demonstrate that OML outperforms standard methods on tasks like Split-Omniglot, significantly boosting continual learning efficiency.

Overview of "Meta-Learning Representations for Continual Learning"

The paper "Meta-Learning Representations for Continual Learning" by Javed and White tackles the pervasive challenge of catastrophic forgetting in continual learning systems. Continual learning requires an agent to continuously assimilate new information while retaining old knowledge—a difficult endeavor for neural networks prone to overwriting past learning with new updates. The authors propose a novel methodology named "Online aware Meta-Learning" (OML) designed to develop representations inherently capable of mitigating catastrophic interference and fostering accelerated future learning.

Methodology

The central innovation of the paper is the OML objective, which explicitly trains neural representations to be resilient against interference and conducive to ongoing updates. This contrasts with prevalent methods which passively employ rehearsal or regularization techniques to prevent forgetting or promote knowledge retention. The paper argues that using the representation itself as a training signal can yield representations that are naturally sparse, thereby promoting greater robustness to interference.

The OML algorithm optimizes representation learning through a novel training signal derived directly from the concept of catastrophic interference. This process allows for developing representations that align with the core requirements of continual learning: the ability to incorporate new learning quickly while minimizing interference with existing knowledge.

Notably, the architecture proposed separates the tasks of representation learning (RLN) and prediction learning (PLN), with a meta-objective driving the training of the RLN by evaluating the network's ability to sustain performance across a sequence of tasks without rehearsal.

Experimental Results

The proposed framework was evaluated on tasks including Incremental Sinusoidal Regression and Split-Omniglot classification, comparing OML with several baseline strategies such as standard SGD updates, experience replay methods like MER, and sparse representations like SR-NN. The results consistently demonstrate that OML can maintain robustness against forgetting and improve learning efficiency across new tasks beyond what was achievable with former methods.

Specifically, the OML trained networks excelled in retaining performance over sequences while noted baselines exhibited substantial forgetting. Additionally, OML-enhanced representations yielded significant improvements when combined with existing continual learning strategies, delivering performance boosts in methods like MER and EWC.

Theoretical and Practical Implications

The findings from this paper contribute theoretically by elucidating the role of representation learning in continual contexts and suggesting methodologies that can optimize neural networks for such scenarios. Practically, these insights can be translated into agents operating in dynamic environments, such as robotics or streaming data applications, wherein steadfast knowledge retention during continuous updates is crucial.

Future Directions

The paper opens new avenues for research into modular and hybrid approaches for representation learning, emphasizing continuous adaptation and resilience. The concept of periodically refining network representations through sleep phases or offline epochs appears a promising simplification of the meta-training process, potentially leading to more practical implementations in real-world neural networks.

Moreover, exploring how alternative computational paradigms, such as attention mechanisms, can further enhance the dynamics addressed by OML would be a promising extension of this work. Such advancements could provide a pathway to robustly modulating the granularity and fidelity of updates, reducing interference, and pushing the boundaries of what is achievable with continual learning systems.

In conclusion, the proposed OML framework represents a significant step forward in realizing neural systems capable of lifelong learning, with strong implications for both theoretical research and applied intelligent systems.