Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning
Authors: Nisha L. Raichur, Lucas Heublein, Tobias Feigl, Alexander Rügamer, Christopher Mutschler, Felix Ott
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results conducted on the CIFAR-10, CIFAR-100, and Image Net100 datasets for image classification and images of a GNSS-based dataset for interference classification validate the efficacy of our method, showcasing its superiority over existing state-of-the-art approaches. |
| Researcher Affiliation | Academia | Nisha L. Raichur EMAIL Fraunhofer Institute for Integrated Circuits IIS, Nürnberg, Germany |
| Pseudocode | Yes | Algorithm 1 BLCL: Algorithm for Class-Incremental Learning (Python & Py Torch like code) |
| Open Source Code | Yes | Git: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/gnss_class_incremental_learning |
| Open Datasets | Yes | Experimental results conducted on the CIFAR-10, CIFAR-100, and Image Net100 datasets for image classification and images of a GNSS-based dataset for interference classification validate the efficacy of our method, showcasing its superiority over existing state-of-the-art approaches. The CIFAR-10 (Krizhevsky & Hinton, 2009) dataset consists of 60,000 colour images of size 32 32. The CIFAR-100 (Krizhevsky & Hinton, 2009) dataset has 100 classes containing 600 images each, and hence, we train 10 classes per task with 10 tasks in total. Image Net100 is a subset of the larger Image Net dataset, containing 100 carefully selected classes with 1,300 images per class, maintaining a balanced distribution. It is commonly used for benchmarking due to its reduced computational requirements while preserving the diversity of Image Net (Deng et al., 2009). |
| Dataset Splits | Yes | We partition the dataset into a 64% training set, 16% validation set, and a 20% test set split (balanced over the classes). We train five tasks: task 1 consists of the classes 0, 1, and 2, task 2 consists of the classes 3 and 4, task 3 consists of the classes 5 and 6, task 4 consists of the classes 7 and 8, and task 5 consists of the classes 9 and 10. |
| Hardware Specification | Yes | All experiments are conducted utilizing Nvidia Tesla V100-SXM2 GPUs with 32 GB VRAM, equipped with Core Xeon CPUs and 192 GB RAM. |
| Software Dependencies | No | The paper mentions "Python & Py Torch like code" in Algorithm 1 and "torchvision.transforms" in Appendix A.1, but no specific version numbers are provided for these or any other software dependencies. |
| Experiment Setup | Yes | We use the vanilla Adam optimizer with a learning rate set to 0.1, a decay rate of 0.1, a batch size of 128, and train each task for 300 epochs. |