Gradient based sample selection for online continual learning
Authors: Rahaf Aljundi, Min Lin, Baptiste Goujaud, Yoshua Bengio
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments This section serves to validate our approach and show its effectiveness at dealing with continual learning problems where task boundaries are not available. |
| Researcher Affiliation | Academia | Rahaf Aljundi KU Leuven rahaf.aljundi@gmail.com Min Lin Mila mavenlin@gmail.com Baptiste Goujaud Mila baptiste.goujaud@gmail.com Yoshua Bengio Mila yoshua.bengio@mila.quebec |
| Pseudocode | Yes | Algorithm 1 IQP Sample Selection; Algorithm 2 Greedy Sample Selection |
| Open Source Code | Yes | The code is available at https://github.com/rahafaljundi/Gradient-based-Sample-Selection |
| Open Datasets | Yes | Disjoint MNIST: MNIST dataset divided into 5 tasks based on the labels with two labels in each task. Permuted MNIST: We perform 10 unique permutations on the pixels of the MNIST images. Disjoint CIFAR-10: Similar to disjoint MNIST, the dataset is split into 5 tasks according to the labels, with two labels in each task. |
| Dataset Splits | No | The paper states training and testing details, but does not explicitly provide information about a separate validation set split (e.g., percentages, counts, or explicit mention of a validation set). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using SGD optimizer and a neural network framework implicitly (PyTorch/TensorFlow are common in such papers, but not specified), but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In all experiments, we use a fixed batch size of 10 samples and perform few iterations over a batch (1-5)... Following [10], for disjoint and permuted MNIST we use a two-layer neural network with 100 neurons each while for CIFAR-10 we use Res Net18... In all experiments, we use SGD optimizer with a learning rate of 0.05 for disjoint MNIST and permuted MNIST and 0.01 for disjoint Cifar-10. |