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..
Efficient Source-free Unlearning via Energy-Guided Data Synthesis and Discrimination-Aware Multitask Optimization
Authors: Xiuyuan Wang, Chaochao Chen, Weiming Liu, Xinting Liao, Fan Wang, Xiaolin Zheng
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three benchmark datasets demonstrate that DSDA outperforms existing unlearning methods, validating its effectiveness and efficiency in source-free unlearning. |
| Researcher Affiliation | Academia | 1Zhejiang University, China. Correspondence to: Chaochao Chen <EMAIL>. |
| Pseudocode | Yes | The pseudocode are shown in Algorithm 1 |
| Open Source Code | No | The paper does not contain any explicit statements regarding the availability of source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We conduct experiments on CIFAR-10, CIFAR-100 (Krizhevsky et al., 2009) and Pins Face Recognition (Hereis, 2024) datasets. |
| Dataset Splits | No | The paper mentions using CIFAR-10, CIFAR-100, and Pins Face Recognition datasets, which are standard benchmarks. However, it does not explicitly state the specific training/validation/test splits, percentages, or sample counts used for these datasets within the main text. |
| Hardware Specification | Yes | All experiments are conducted on two NVIDIA RTX 3090 GPUs and repeated three times with different random seeds. |
| Software Dependencies | No | We implement all experiments in Python 3.9 and use the Py Torch library (Paszke et al., 2019). |
| Experiment Setup | Yes | Both the original and retrained models are trained from scratch using a multi-step learning rate scheduler, which begins with a learning rate of 0.01, and optimized with the Adam optimizer (Kingma & Ba, 2014). For a fair comparison, the batch sizes of all methods are set to 256 in Res Net18 and 32 in Vi T. |