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..
Scalable and Order-robust Continual Learning with Additive Parameter Decomposition
Authors: Jaehong Yoon, Saehoon Kim, Eunho Yang, Sung Ju Hwang
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our network with APD, APD-Net, on multiple benchmark datasets against state-of-the-art continual learning methods, which it largely outperforms in accuracy, scalability, and order-robustness. |
| Researcher Affiliation | Collaboration | Jaehong Yoon1, Saehoon Kim2, Eunho Yang1,2, and Sung Ju Hwang1,2 KAIST1, AITRICS2, South Korea EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Continual learning with Additive Parameter Decomposition |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | 1) CIFAR-100 Split (Krizhevsky & Hinton, 2009) consists of images from 100 generic object classes. 2) CIFAR-100 Superclass consists of images from 20 superclasses of the CIFAR-100 dataset. 3) Omniglot-rotation (Lake et al., 2015) contains OCR images of 1,200 characters (we only use the training set) from various writing systems for training |
| Dataset Splits | Yes | 1) CIFAR-100 Split (Krizhevsky & Hinton, 2009) consists of images from 100 generic object classes. We split the classes into 10 group, and consider 10-way multi-class classification in each group as a single task. We use 5 random training/validation/test splits of 4,000/1,000/1,000 samples. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using exponential learning rate decay and weight decay, but does not specify any software libraries or their version numbers (e.g., PyTorch, TensorFlow, scikit-learn versions). |
| Experiment Setup | Yes | We used exponential learning rate decay at each epoch and all models are applied on weight decay with λ = 1e 4. All hyperparameters are deterimined from a validation set. For MNIST-Variation, we used two-layered feedforward networks with 312, 128 neurons. Training epochs are 50 for all baselines and APDs. λ1 = [2e 4, 1e 4] on APD. For CIFAR-100 Split and CIFAR-100 Superclass, we used Le Net with 20-50-800-500 neurons. Training epochs are 20 for all models. λ1 = [6e 4, 4e 4]. We equally set λ2 = 100, also K=2 per 5 tasks, and β=1e 2 for hierarchical knowledge consolidation on MNIST-Variation, CIFAR-100 Split, and CIFAR-100 Superclass. |