Learning Local Invariant Mahalanobis Distances
Authors: Ethan Fetaya, Shimon Ullman
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compared running the optimization with an SVM solver (Chang & Lin, 2011), to solving it as a semidefinite problem and as a quadratic problem (relaxing the semidefinite constraint). The MNIST dataset is a well known digit recognition dataset, comprising of 28 28 grayscale images on which we perform deskewing preprocessing. For each of the 60,000 training images we computed a local Mahalanobis distance and local invariant Mahalanobis, by training each image separately using all the non-class training images as negative examples (ignoring all same-class images). As can be seen in table 1, we perform much better than exemplar SVM and are comparable with MLMNN. |
| Researcher Affiliation | Academia | Ethan Fetaya ETHAN.FETAYA@WEIZMANN.AC.IL Weizmann Institute of science Shimon Ullman SHIMON.ULLMAN@WEIZMANN.AC.IL Weizmann Institute of science |
| Pseudocode | No | The paper describes algorithms mathematically and through prose, but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology. There are no repository links or explicit statements about code availability. |
| Open Datasets | Yes | The MNIST dataset is a well known digit recognition dataset, comprising of 28 28 grayscale images on which we perform deskewing preprocessing. LFW is a challenging dataset containing 13,233 face images of 5749 different individuals with a high level of variability. We used the aligned images (Huang et al., 2012) which we represented using HOG features (Dalal & Triggs, 2005). |
| Dataset Splits | No | For each of the 60,000 training images we computed a local Mahalanobis distance and local invariant Mahalanobis, by training each image separately using all the non-class training images as negative examples (ignoring all same-class images). The LFW dataset is divided into 10 subsets, when the task is to classify 600 pairs of images from one subset to same/not-same using the other 9 subsets as training data. |
| Hardware Specification | No | The paper discusses memory usage (e.g., '24.6Gb') and mentions running solvers on machines, but does not provide specific hardware details such as CPU/GPU models or memory specifications of the experimental machines. |
| Software Dependencies | No | The paper mentions software like 'LIBSVM (Chang & Lin, 2011)', 'YALMIP', and 'SCS (O Donoghue et al., 2013)' but does not provide specific version numbers for these tools. |
| Experiment Setup | Yes | For each of the 60,000 training images we computed a local Mahalanobis distance and local invariant Mahalanobis, by training each image separately using all the non-class training images as negative examples (ignoring all same-class images). For local invariance, we used 8 one-pixel translation and 6 small rotations as our transformations. At test time we performed knn classification with k = 3 using the local metrics. We used the aligned images (Huang et al., 2012) which we represented using HOG features (Dalal & Triggs, 2005). For each test pair (x1, x2) we compute the their local Mahalanobis matrices, M1 and M2, using the training set and use (x2 x1)T M1(x2 x1)+(x1 x2)T M2(x1 x2) as their similarity score. |