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..
AutoLoRa: An Automated Robust Fine-Tuning Framework
Authors: Xilie Xu, Jingfeng Zhang, Mohan Kankanhalli
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical evaluations demonstrate that our proposed automated RFT disentangled via the Lo Ra branch (Auto Lo Ra) achieves new state-of-the-art results across a range of downstream tasks. Our comprehensive experimental results validate that our proposed automated robust fine-tuning disentangled via a Lo Ra branch (Auto Lo Ra) is effective in improving adversarial robustness among various downstream tasks. |
| Researcher Affiliation | Academia | 1 School of Computing, National University of Singapore 2 School of Computer Science, The University of Auckland 3 RIKEN Center for Advanced Intelligence Project (AIP) |
| Pseudocode | Yes | Algorithm 1 Automated RFT disentangled via a Lo Ra branch (Auto Lo Ra) |
| Open Source Code | Yes | Our source code is available at the Git Hub. |
| Open Datasets | Yes | We considered six datasets as the downstream tasks. ➊CIFAR-10 with 10 classes and ➋CIFAR-100 (Krizhevsky, 2009) with 100 classes are low-resolution image datasets... ➌Describable textures dataset with 57 classes (DTD-57) (Cimpoi et al., 2014) is a collection of high-resolution textural images... ➍Stanford Dogs dataset with 120 dog categories (DOG-120) (Khosla et al., 2011)... ➎Caltech-UCSD Birds-200-2011 with 200 categories of birds (CUB200) (Wah et al., 2011)... ➏Caltech-256 with 257 classes (Griffin et al., 2007)... |
| Dataset Splits | Yes | We randomly selected 5% of the entire training data as the validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | For the fair comparison, we set maximum training epoch E = 60 following (Liu et al., 2023). We used SGD as the optimizer, froze the weight decay of SGD as 1e 4, and set the rank of the Lo Ra branch rnat = 8 by default. We set α = 1.0 and λmax 2 = 6.0 by default. We randomly selected 5% of the entire training data as the validation set. During training, we used PGD-10 with an adversarial budget of 8/255 and step size of 2/255 to generate the adversarial training and validation data. |