Optimizing affinity-based binary hashing using auxiliary coordinates
Authors: Ramin Raziperchikolaei, Miguel A. Carreira-Perpinan
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared to this, our optimization is guaranteed to obtain better hash functions while being not much slower, as demonstrated experimentally in various supervised datasets. |
| Researcher Affiliation | Academia | Ramin Raziperchikolaei EECS, University of California, Merced rraziperchikolaei@ucmerced.edu Miguel A. Carreira-Perpi n an EECS, University of California, Merced mcarreira-perpinan@ucmerced.edu |
| Pseudocode | No | The supplementary material gives the overall MAC algorithm to learn a hash function by optimizing an affinity-based loss function. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | (1) CIFAR [13] contains 60 000 images in 10 classes. We use D = 320 GIST features [23] from each image. We use 58 000 images for training and 2 000 for test. (2) Infinite MNIST [20]. We generated, using elastic deformations of the original MNIST handwritten digit dataset, 1 000 000 images for training and 2 000 for test, in 10 classes. |
| Dataset Splits | Yes | (1) CIFAR [13] contains 60 000 images in 10 classes. ... We use 58 000 images for training and 2 000 for test. (2) Infinite MNIST [20]. ... We generated ... 1 000 000 images for training and 2 000 for test, in 10 classes. We train the hash functions in a subset of 10 000 points of the training set, and report precision and recall by searching for a test query on the entire dataset (the base set). |
| Hardware Specification | No | The runtime per iteration for our 10 000-point training sets with b = 48 bits and κ+ = 100 and κ = 500 neighbors in a laptop is 2 for both MACcut and MACquad. |
| Software Dependencies | No | As hash functions (for each bit), we use linear SVMs (trained with LIBLINEAR; [9]) and kernel SVMs (with 500 basis functions). |
| Experiment Setup | Yes | We use the following schedule for the penalty parameter µ in the MAC algorithm (regardless of the hash function type or dataset). We initialize Z with µ = 0, i.e., the result of quad or cut. Starting from µ1 = 0.3 (MACcut) or 0.01 (MACquad), we multiply µ by 1.4 after each iteration (Z and h step). |