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..
Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding
Authors: Depeng Li, Tianqi Wang, Junwei Chen, Qining Ren, Kenji Kawaguchi, Zhigang Zeng
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method achieves competitive accuracy performance, even with absolute superiority of zero exemplar buffer and 1.02 the base model. [...] Empirical evaluation across a range of widely used benchmark datasets demonstrates the superiority of our approach in terms of exemplar buffers, network expansion, and competitive performance. [...] We perform extensive experiments to evaluate the proposed CLDNet in the challenging class-IL setting. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence and Automation, Huazhong University of Science and Technology 2School of Computing, National University of Singapore |
| Pseudocode | Yes | Algorithm 1: CLDNet Training and Test algorithm |
| Open Source Code | No | The paper does not explicitly state that source code is released or provide a link to a code repository. |
| Open Datasets | Yes | Small Scale: MNIST (Le Cun et al. 1998) contains 60,000 handwritten digit images in the training set and 10,000 samples in the test set... Fashion MNIST (Xiao, Rasul, and Vollgraf 2017)... CIFAR-10 (Krizhevsky, Hinton et al. 2009)... Medium Scale: CIFAR-100 (Krizhevsky, Hinton et al. 2009)... Large Scale: Image Net-R (Hendrycks et al. 2021)... |
| Dataset Splits | No | The paper specifies overall training and testing set sizes and how classes are divided into tasks but does not explicitly detail a validation split or its size/percentage for reproducibility. |
| Hardware Specification | Yes | All experiments are run in Py Torch using NVIDIA RTX 3080-Ti GPUs with 12GB memory. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version or the versions of other ancillary software components used for reproducibility. |
| Experiment Setup | Yes | In our CLDNet, for HBO we set the coefficient β = 500 and adopt the Gaussian kernel as suggested by (Ma, Lewis, and Kleijn 2020), as well as the adaptive α with an initial value 0.01 for the orthogonal projector, like (Guo et al. 2022); For EAE we set γ = 0.04, d = 1000, and C = 1000 following the recommendations by EBVs (Shen, Sun, and Wei 2023). |