EEC: Learning to Encode and Regenerate Images for Continual Learning
Authors: Ali Ayub, Alan Wagner
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We tested and compared EEC to several SOTA approaches on four benchmark datasets: MNIST, SVHN, CIFAR-10 and Image Net-50. We also report the memory used by our approach and its performance in restricted memory conditions. Finally, we present an ablation study to evaluate the contribution of different components of EEC. |
| Researcher Affiliation | Academia | Ali Ayub, Alan R. Wagner The Pennsylvania State University State College, PA, USA, 16803 {aja5755,alan.r.wagner}@psu.edu.edu |
| Pseudocode | Yes | A EEC ALGORITHMS The algorithms below describe portions of the complete EEC algorithm. Algorithm 1 is for autoencoder training (Section 3.1 in paper), Algorithm 2 is for memory integration (Section 3.2 in paper), Algorithm 3 is for rehearsal, pseudo-rehearsal and classifier training (Section 3.3 in paper) and Algorithm 4 is for filtering pseudo-images (Section 3.3 in paper). |
| Open Source Code | No | The paper does not provide a specific link to an open-source code repository or explicitly state that the code for their methodology is made publicly available. |
| Open Datasets | Yes | The MNIST dataset consists of grey-scale images of handwritten digits between 0 to 9, with 50,000 training images, 10,000 validation images and 10,000 test images. CIFAR-10 consists of 50,000 RGB training images and 10,000 test images belonging to 10 object classes. Image Net-50 is a smaller subset of the i LSVRC-2012 dataset containing 50 classes with 1300 training images and 50 validation images per class. All of these are well-known, publicly available benchmark datasets in machine learning. |
| Dataset Splits | Yes | The MNIST dataset consists of grey-scale images of handwritten digits between 0 to 9, with 50,000 training images, 10,000 validation images and 10,000 test images. Image Net-50 is a smaller subset of the i LSVRC-2012 dataset containing 50 classes with 1300 training images and 50 validation images per class. |
| Hardware Specification | Yes | We used Pytorch (Paszke et al., 2019) and an Nvidia Titan RTX GPU for implementation and training of all neural network models. |
| Software Dependencies | No | The paper mentions using “Pytorch (Paszke et al., 2019)” but does not provide a specific version number for PyTorch or other key software components, which is required for reproducibility. |
| Experiment Setup | Yes | Hyperparameter values and training details are reported in Appendix C. Table 4: Hyper-parameters for EEC autoencoder training; Table 5: Hyper-parameters for EEC classifier training. These tables specify values for parameters such as number of epochs, learning rate, batch size, optimizer, and weight decay. |