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..
TriRE: A Multi-Mechanism Learning Paradigm for Continual Knowledge Retention and Promotion
Authors: Preetha Vijayan, Prashant Bhat, Bahram Zonooz, Elahe Arani
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Setup: We expand the Mammoth CL repository in Py Torch [9]. On the basis of Class-IL and Task-IL scenarios, we assess the existing CL techniques against the proposed one. Experimental Results: We compare Tri RE with contemporary rehearsal-based and weight regularization methods in Class-IL and Task-IL settings. As shown in Table 1, Tri RE consistently outperforms |
| Researcher Affiliation | Collaboration | Preetha Vijayan1, , Prashant Bhat2,3,*, Bahram Zonooz2,3, , Elahe Arani2, 1Nav Info Europe 2Eindhoven University of Technology (TU/e) 3Tom Tom |
| Pseudocode | Yes | Algorithm 1 Proposed Approach Tri RE |
| Open Source Code | Yes | Code is available at https://github.com/Neur AI-Lab/Tri RE |
| Open Datasets | Yes | To evaluate the performance of our method in Task-IL and Class-IL scenarios, we employ three different datasets: Seq-CIFAR10, Seq-CIFAR100, and Seq-Tiny Image Net. These datasets are derived from CIFAR10, CIFAR100, and Tiny Image Net, respectively. |
| Dataset Splits | Yes | The hyperparameters required to replicate the results of Tri RE can be found in Table 5. These hyperparameters were determined through a tuning process involving different random initializations and a small portion of the training set reserved for validation. |
| Hardware Specification | Yes | Table 7 specifically lists the training times required to learn a single task (the first) for 3 epochs on an NVIDIA RTX 2080 Ti for Seq-CIFAR100 dataset with a buffer size of 200. |
| Software Dependencies | No | The paper mentions using 'Py Torch' but does not specify a version number for it or any other software dependencies. |
| Experiment Setup | Yes | The hyperparameters required to replicate the results of Tri RE can be found in Table 5... All experiments were conducted using a batch size of 32 and trained for 50 epochs. Tri RE was optimized using the Adam optimizer [24] implemented in Py Torch. Furthermore, the number of epochs allocated to each phase specified in Algorithm 1 was consistently set at a ratio of E1 : E2 : E3 = 3 : 1 : 1. |