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..
EEC: Learning to Encode and Regenerate Images for Continual Learning
Authors: Ali Ayub, Alan Wagner
ICLR 2021 | Venue PDF | 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 EMAIL |
| 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. |