RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval
Authors: Yihan Wu, Hongyang Zhang, Heng Huang
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on image retrieval tasks validate the robustness of our Retrieval Guard method. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, University of Pittsburgh, USA 2David R. Cheriton School of Computer Science, University of Waterloo, Canada. |
| Pseudocode | Yes | Algorithm 1 Certified Radius by Retrieval Guard |
| Open Source Code | No | The paper does not provide any links to source code or explicitly state that code is made publicly available. |
| Open Datasets | Yes | We run experiments with a popular dataset Online Products of metric learning (Song et al., 2016)... The experiments with CUB200 (Wah et al., 2011) and CARS196 (Krause et al., 2013) are listed in Appendix C. |
| Dataset Splits | No | The paper specifies training and test set splits for the datasets but does not explicitly mention or provide details for a validation set split. |
| Hardware Specification | Yes | The running time of Retrieval Guard for a single image evaluation with 100,000 Monte-Carlo samples on a 24GB Nvidia Tesla P40 GPU is about 3 minutes. |
| Software Dependencies | No | The paper mentions using "ResNet50 architecture" and adapting a "DML framework from (Roth et al., 2020)" but does not specify version numbers for any software dependencies like PyTorch, TensorFlow, Python, or CUDA. |
| Experiment Setup | Yes | The embedding dimension k is 128 and the number of training epochs is 100. The learning rate is 1e-5 with multi-step learning rate scheduler 0.3 at the 30-th, 55-th, and 75-th epochs. We select the initial value of β as 1.2, learning rate of β as 0.0005, and γ = 0.2 in the margin loss. We also test the model performance under Gaussian noise with σ = 0.1, 0.25, 0.5, 1. As the embedding of metric learning models is ℓ2 normalized, we have F = 1. For each sample, we generate 100,000 Monte-Carlo samples to estimate g(x). The confidence level α is chosen as 0.01. |