Loss Decoupling for Task-Agnostic Continual Learning
Authors: Yan-Shuo Liang, Wu-Jun Li
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that LODE can outperform existing state-of-the-art replay-based methods on multiple continual learning datasets. |
| Researcher Affiliation | Academia | Yan-Shuo Liang and Wu-Jun Li National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, P. R. China liangys@smail.nju.edu.cn,liwujun@nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Loss Decoupling (LODE) for Continual Learning |
| Open Source Code | No | The paper states it's built on an existing repository ('mammoth [7] continual learning repository in Py Torch') but does not explicitly state that the code for their proposed method (LODE) is open-source or provided. |
| Open Datasets | Yes | Datasets We use three popular datasets for evaluation, including Seq-CIFAR10 [3], Seq CIFAR100 [10], and Seq-Tiny Image Net [22]. |
| Dataset Splits | Yes | For each task, 5% of the training samples are divided into a validation set. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments, such as particular GPU or CPU models. |
| Software Dependencies | No | The paper mentions using 'Py Torch' but does not provide its version number or specific versions for other software dependencies. |
| Experiment Setup | Yes | We use stochastic gradient descent (SGD) to optimize the parameters. The batch size and replay size are set to 32... We also follow existing methods [7, 5] to set memory as 500 and 5120 for all the datasets. |