Look-ahead Meta Learning for Continual Learning
Authors: Gunshi Gupta, Karmesh Yadav, Liam Paull
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate La-MAML in settings where the model has to learn a set of sequentially streaming classification tasks. |
| Researcher Affiliation | Academia | Gunshi Gupta Mila, Ude M guptagun@mila.quebec Karmesh Yadav * Carnegie Mellon University karmeshy@andrew.cmu.edu Liam Paull Mila, Ude M paulll@iro.umontreal.ca |
| Pseudocode | Yes | Algorithm 1 La-MAML : Look-ahead MAML |
| Open Source Code | Yes | The code for our algorithm can be found at: https://github.com/montrealrobotics/La-MAML |
| Open Datasets | Yes | MNIST Rotations, introduced in [17], comprises tasks to classify MNIST digits rotated by a different common angle in [0, 180] degrees in each task. In MNIST Permutations, tasks are generated by shuffling the image pixels by a fixed random permutation... We conduct experiments on the CIFAR-100 dataset... We also evaluate on the Tiny Imagenet-200 dataset... |
| Dataset Splits | Yes | Each task consists of 1000 images for MNIST Permutations and Rotations and 200 images for Many Permutations for training and validation and 2000 images for testing. |
| Hardware Specification | No | No specific hardware details (like GPU or CPU models, or cloud instance types) used for experiments were mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) needed for replication. |
| Experiment Setup | Yes | We use a fully connected network with three hidden layers (256 units per layer) and ReLU non-linearities for the MNIST experiments. For Tiny Imagenet and CIFAR-100, we use a ResNet-18... For the CIFAR-100 and Tiny Imagenet experiments, we use a batch size of 64 and train for 50 epochs. Each method is allowed a replay-buffer, containing upto 200 and 400 samples for CIFAR-100 and Tiny Imagenet respectively. |