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..
One-Pixel Shortcut: On the Learning Preference of Deep Neural Networks
Authors: Shutong Wu, Sizhe Chen, Cihang Xie, Xiaolin Huang
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate OPS and its counterparts in 6 architectures, 6 model sizes, 8 training strategies on CIFAR-10 (Krizhevsky et al., 2009) and Image Net (Russakovsky et al., 2015) subset, and find that OPS is always superior in degrading model s testing accuracy than EM ULEs. |
| Researcher Affiliation | Academia | Shutong Wu 1, Sizhe Chen 1, Cihang Xie2 & Xiaolin Huang 1 1Department of Automation, Shanghai Jiao Tong University 2Computer Science and Engineering, University of California, Santa Cruz |
| Pseudocode | Yes | Algorithm 1 Model-Free Searching for One-Pixel Shortcut |
| Open Source Code | Yes | code available at https://github.com/cychomatica/One-Pixel-Shotcut. |
| Open Datasets | Yes | We evaluate OPS and its counterparts in 6 architectures, 6 model sizes, 8 training strategies on CIFAR-10 (Krizhevsky et al., 2009) and Image Net (Russakovsky et al., 2015) subset |
| Dataset Splits | Yes | Table 1: The testing accuracy of Res Net-18 models trained on unshuffled and shuffled data. ... We train different convolutional networks and vision transformers on the One-Pixel Shortcut CIFAR-10 training set, and evaluate their performance on the unmodified CIFAR-10 test set. |
| Hardware Specification | Yes | Our experiments are implemented on CIFAR-10 and Image Net subset, using 4 NVIDIA RTX 2080Ti GPUs. |
| Software Dependencies | No | The paper mentions optimizers like SGD and Adam W but does not specify software versions for libraries like PyTorch, TensorFlow, or specific Python versions. |
| Experiment Setup | Yes | For all the convolutional networks, we use an SGD optimizer with a learning rate set to 0.1, momentum set to 0.9, and weight decay set to 5e 4. For all the compact vision transformers, we use Adam W optimizer with β1 = 0.9, β2 = 0.999, learning rate set to 5e 4, and weight decay set to 3e 2. Batch size is set to 128 for all the models except Wide Res Net-28-10, where it is set to 64. ... All the models are trained for 200 epochs with a multi-step learning rate schedule, and the training accuracy of each model is guaranteed to reach near 100%. |