Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Loss Decoupling for Task-Agnostic Continual Learning
Authors: Yan-Shuo Liang, Wu-Jun Li
NeurIPS 2023 | Venue PDF | 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 EMAIL,EMAIL |
| 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. |