Efficient end-to-end learning for quantizable representations
Authors: Yeonwoo Jeong, Hyun Oh Song
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results on Cifar-100 and on Image Net datasets show the state of the art search accuracy in precision@k and NMI metrics while providing up to 98 and 478 search speedup respectively over exhaustive linear search. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Seoul National University, Seoul, Korea. |
| Pseudocode | Yes | Algorithm 1 Learning algorithm |
| Open Source Code | Yes | The source code is available at https://github.com/maestrojeong/Deep-Hash Table-ICML18. |
| Open Datasets | Yes | We report our results on Cifar-100 (Krizhevsky et al., 2009) and Image Net (Russakovsky et al., 2015) datasets |
| Dataset Splits | Yes | Cifar-100 (Krizhevsky et al., 2009) dataset has 100 classes. Each class has 500 images for train and 100 images for test. ... Image Net ILSVRC-2012 (Russakovsky et al., 2015) dataset has 1, 000 classes and comes with train (1, 281, 167 images) and val set (50, 000 images). We use the first nine splits of train set to train our model, the last split of train set for validation, and use validation dataset to test the query performance. |
| Hardware Specification | Yes | Each data point is averaged over 20 runs on machines with Intel Xeon E5-2650 CPU. |
| Software Dependencies | No | The paper mentions "Tensorflow (Abadi et al., 2015)" and "OR-Tools (Google optimization tools for combinatorial optimization problems) (OR-tools, 2018)" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The batch size is set to 128. The metric learning base model is trained for 175k iterations, and learning rate decays to 0.1 of previous learning rate after 100k iterations. We finetune the base model for 70k iterations and decayed the learning rate to 0.1 of previous learning rate after 40k iterations. |