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 [1].
DDGR: Continual Learning with Deep Diffusion-based Generative Replay
Authors: Rui Gao, Weiwei Liu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments in class incremental (CI) and class incremental with repetition (CIR) settings demonstrate the advantages of DDGR. Our code is available at https://github.com/ xiaocangsheng GR/DDGR. |
| Researcher Affiliation | Academia | 1School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China. |
| Pseudocode | Yes | Algorithm 1 Deep Diffusion-based Generative Replay and Algorithm 2 Instruction Process |
| Open Source Code | Yes | Our code is available at https://github.com/ xiaocangsheng GR/DDGR. |
| Open Datasets | Yes | We conduct experiments on two widely used datasets: CIFAR-100 and Image Net (Deng et al., 2009). Moreover, in CIR scenario, we use CORe50 (Lomonaco & Maltoni, 2017) to conduct experiments. |
| Dataset Splits | No | The paper describes how datasets are divided into tasks for continual learning scenarios, such as 'initial task consists of 50 random classes' and 'each subsequent task contains five classes'. It defines evaluation on a 'test dataset S0:i test' but does not explicitly detail a separate validation split or the precise sizes/percentages of train/test/validation splits for each task. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as programming language versions or library versions (e.g., Python 3.x, PyTorch x.x). |
| Experiment Setup | No | The paper describes model architectures used (Res Net, Alex Net, UNet) and discusses task-based learning settings, but it does not explicitly provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) for the experimental setup. |