Adversarial Metric Learning
Authors: Shuo Chen, Chen Gong, Jian Yang, Xiang Li, Yang Wei, Jun Li
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on toy data and practical datasets clearly demonstrate the superiority of AML to representative state-of-the-art metric learning models. In this section, empirical investigations are conducted to validate the effectiveness of AML. In detail, we first visualize the mechanism of the proposed AML on a synthetic dataset. Then we compare the performance of the proposed method AML (Algorithm 1) with three classical metric learning methods (ITML [Davis et al., 2007], LMNN [Weinberger and Saul, 2009] and Flat Geo [Meyer et al., 2011]) and five state-of-the-art metric learning methods (RVML [Perrot and Habrard, 2015], GMML [Zadeh et al., 2016], ERML [Yang et al., 2016], DRML [Harandi et al., 2017], and DRIFT [Ye et al., 2017]) on seven benchmark classification datasets. Next, all methods are compared on three practical datasets related to face verification and image matching. Finally, the parametric sensitivity of AML is studied. |
| Researcher Affiliation | Academia | 1School of Computer Science & Engineering, Nanjing University of Science & Technology 2Key Laboratory of Intelligent Perception & Systems for High-Dimensional Information 3Jiangsu Key Laboratory of Image & Video Understanding for Social Security {shuochen, chen.gong, csjyang, xiang.li.implus, csywei}@njust.edu.cn, junl.mldl@gmail.com |
| Pseudocode | Yes | Algorithm 1 Solving AML in Eq. (6) via gradient descent. |
| Open Source Code | No | The paper does not provide any information about open-source code for the described methodology. There are no links to repositories or explicit statements about code availability. |
| Open Datasets | Yes | The datasets are from the well-known UCI repository [Asuncion and Newman, 2007], which include Breast-Cancer, Vehicle, German-Credit, Image-Segment, Isolet, Letters and MNIST. The Pub Fig face dataset [Nair and Hinton, 2010] consists of of 2 104 pairs of images belonging to 140 people, in which the first 80% pairs are selected for training and the rest are used for testing. Similar experiments are performed on the LFW face dataset [Huang et al., 2007] which includes 13233 unconstrained face images of 5749 individuals. The image matching dataset MVS [Brown et al., 2011] consists of 64 64 gray-scale image sampled from 3D reconstructions of the Statue of Liberty (LY), Notre Dame (ND) and Half Dome in Yosemite (YO). |
| Dataset Splits | Yes | In each trial, 80% of examples are randomly selected as the training examples, and the rest are used for testing. The Pub Fig face dataset [Nair and Hinton, 2010] consists of of 2 104 pairs of images belonging to 140 people, in which the first 80% pairs are selected for training and the rest are used for testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions features extracted by DSIFT and Siamese-CNN, but does not specify any software packages or libraries with version numbers (e.g., Python, PyTorch, TensorFlow, scikit-learn, etc.) used in the implementation or for experiments. |
| Experiment Setup | Yes | The step size ρ is recommended to be fixed to 0.001 in our experiments. The parameters in our method such as α and β are tuned by searching the grid {10 3, 10 2, , 103}. the training pairs are generated by randomly picking up 1000c(c 1) pairs among the training examples. we follow existing works [Xie and Xing, 2013; Zhan et al., 2016] and adopt the k-NN classifier (k = 5) based on the learned metrics to investigate the classification error rate. |