Meta-Learning Representations for Continual Learning
K. Javed and M. White

Paper Code Talk Poster


  • Artificial Neural Networks trained online on a correlated stream of data suffer from catastrophic interference.
  • We propose learning a representation that is robust to catastrophic interference.
  • To learn the representation, we propose MRCL, a second order meta-learning objective that directly minimizes interference and maximizes forward transfer.
  • Highly sparse representations naturally emerge by minimizing our proposed objective. Moreover, these representations are highly effective at reducing interference and forgetting on continual learning benchmarks.


  • We hypothesize that in the observation space, gradient w.r.t one task interference with other tasks, and by learning a representation in which solution manifolds of different tasks are either parallel or orthogonal, we can mitigate catastrophic interference as shown in Figure 1.
  • Figure 1 : We want to find a representation such that solution manifold of different tasks are parallel or orthogonal.
  • More concretely, we propose transforming the input into R, a large d dimensional vector, using a deep Representation Learning Network (RLN) such that it is possible to learn without interference from R. An example architecture of our method is shown in Figure 2.
  • To train RLN, we treat the parameters in the RLN as meta-parameters which are updated to minimize interference when learning on correlated sequences of data using a Task Learning Network (TLN).
Figure 2 : One possible realization of an architecture used by MRCL. RLN learns to transform inputs into a space that allows learning without forgetting.


We compare MRCL with a Pretraining, a method that learns a representation by pre-training on the meta-training dataset, and SR-NN, a recent method that learns sparse representations to minimize interference. We report the results in Figure 3.
Figure 3 : Results of training a classifier incrementally (One class at a time) at meta-test time.

Ablation Studies

  • (Left) Meta-learning a network initialization (Similar to MAML) is not sufficient for reducing interference.
  • (Right) Using longer correlated trajectories in the inner loop of meta-learning results in better representations. Our hypothesize is that online updates using long correlated trajectories result in more interference and provide a stronger training signal.
Figure 4 : Ablation studies.

Khurram Javed