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..
Overcoming Catastrophic Forgetting by Incremental Moment Matching
Authors: Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, Byoung-Tak Zhang
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that IMM achieves state-of-the-art performance by balancing the information between an old and a new network. |
| Researcher Affiliation | Collaboration | Sang-Woo Lee1, Jin-Hwa Kim1, Jaehyun Jun1, Jung-Woo Ha2, and Byoung-Tak Zhang1,3 Seoul National University1 Clova AI Research, NAVER Corp2 Surromind Robotics3 |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code for the experiments is available in Github repository1. 1https://github.com/btjhjeon/IMM_tensorflow |
| Open Datasets | Yes | We analyze our approach on a variety of datasets including the MNIST, CIFAR-10, Caltech-UCSDBirds, and Lifelog datasets. |
| Dataset Splits | No | Consider that tuned hyperparameter setting is often used in previous works of continual learning as it is difficult to define a validation set in their setting. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | Hyperparam denotes the main hyperparameter of each algorithm. For IMM with transfer, only α is tuned. The numbers in the parentheses refer to standard deviation. Every IMM uses weight-transfer. Table 1 lists specific hyperparameter values like 'λ in (10)', 'p in (11)', 'α2 in (4)', 'α2 in (7)' for both untuned and tuned settings. |