Elastic Feature Consolidation For Cold Start Exemplar-Free Incremental Learning
Authors: Simone Magistri, Tomaso Trinci, Albin Soutif, Joost van de Weijer, Andrew D. Bagdanov
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on CIFAR-100, Tiny-Image Net, Image Net-Subset and Image Net-1K demonstrate that Elastic Feature Consolidation is better able to learn new tasks by maintaining model plasticity and significantly outperform the state-of-the-art. |
| Researcher Affiliation | Academia | Department of Information Engineering, University of Florence, Italy1 Computer Vision Center, Universitat Autònoma de Barcelona, Spain2 |
| Pseudocode | Yes | In Appendix D we provide the pseudocode of the overall training procedure. (Algorithm 1: Elastic Feature Consolidation) |
| Open Source Code | Yes | Code to reproduce experiments is available at https://github.com/simomagi/elastic_ feature_consolidation |
| Open Datasets | Yes | We consider three standard datasets: CIFAR-100 (Krizhevsky et al., 2009),Tiny-Image Net (Wu et al., 2017) and Image Net-Subset (Deng et al., 2009). Each is evaluated in two settings. |
| Dataset Splits | No | The paper specifies the train/test split for CIFAR-100 (500 images for training and 100 for testing per class), but does not explicitly state a specific validation set split (percentages or sample counts) for any of the datasets used. |
| Hardware Specification | No | The paper does not explicitly state the specific hardware (e.g., GPU models, CPU models, or cloud computing instances with specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Adam optimizer and ResNet-18 backbone, but does not specify software dependencies with version numbers like Python, PyTorch, or CUDA versions. |
| Experiment Setup | Yes | For the incremental steps of EFC we used Adam with weight decay of 2e-4 and fixed learning rate of 1e-4 for Tiny-Image Net and CIFAR-100, while for Image Net-Subset we use a learning rate of 1e-5 for the backbone and 1e-4 for the heads. We fixed the total number of epochs to 100 and use a batch size of 64. We set λEFM = 10 and η = 0.1 in Eq. 9 for all the experiments. |