Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval
Authors: Frederik Warburg, Marco Miani, Silas Brack, Søren Hauberg
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that our Laplacian Metric Learner (LAM) yields well-calibrated uncertainties, reliably detects out-of-distribution examples, and has state-of-the-art predictive performance. |
| Researcher Affiliation | Academia | Frederik Warburg Technical University of Denmark frwa@dtu.dk Marco Miani Technical University of Denmark mmia@dtu.dk Silas Brack Technical University of Denmark silasbrack@gmail.com Søren Hauberg Technical University of Denmark sohau@dtu.dk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We extend Stoch Man [11] with the Hessian backpropagation for the contrastive loss, and the Py Torch [34] code is publicly available2. 2See https://github.com/Frederik Warburg/bayesian-metric-learning |
| Open Datasets | Yes | We evaluate OOD performance on the commonly used benchmarks [31], where we use (1) Fashion MNIST [49] as ID and MNIST [26] as OOD, and (2) CIFAR10 [21] as ID and SVHN [32] as OOD. ... We first test with CUB200 [45] as ID and CAR196 [20] as OOD ... and second, test with LFW [17] as ID and CUB200 as OOD. We evaluate on MSLS [46] |
| Dataset Splits | Yes | We use the standard train/test split, training on 24 cities and testing on six other cities. We follow the zero-shot train/test split [30]. |
| Hardware Specification | Yes | The last-layer LAM training time is 3 hours for online LAM vs 2.3 hours for deterministic contrastive loss on LFW, and 30 minutes vs 15 minutes loss on CUB200 on an NVIDIA RTX A5000. |
| Software Dependencies | No | The paper mentions 'Py Torch [34]' but does not specify a version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | Table 4: Ablation on LFW shows parameters like 'Margin', 'Number of pairs per batch', 'Latent dimension', 'Memory factor α', 'Post-hoc tempering β' with specific values used in the experiments. |