An ensemble diversity approach to supervised binary hashing
Authors: Miguel A. Carreira-Perpinan, Ramin Raziperchikolaei
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | section 3 gives evidence with image retrieval datasets that this simple approach indeed works very well, and section 4 further discusses the connection between hashing and ensembles. 3 Experiments We use the following labeled datasets (all using the Euclidean distance in feature space): (1) CIFAR [19]... (2) Infinite MNIST [29]. |
| Researcher Affiliation | Academia | Miguel A. Carreira-Perpi n an EECS, University of California, Merced mcarreira-perpinan@ucmerced.edu Ramin Raziperchikolaei EECS, University of California, Merced rraziperchikolaei@ucmerced.edu |
| Pseudocode | No | The paper describes algorithms in text (e.g., 'min-cut algorithm (as implemented in [4])'), but does not contain structured pseudocode or an explicitly labeled algorithm block. |
| 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 | We use the following labeled datasets (all using the Euclidean distance in feature space): (1) CIFAR [19] contains 60 000 images in 10 classes... (2) Infinite MNIST [29]. 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 | No | The paper specifies training and test splits for CIFAR (58,000 for training, 2,000 for test) and Infinite MNIST (1,000,000 for training, 2,000 for test) but does not provide details for a separate validation split explicitly used in their main experiments for model selection or early stopping. |
| Hardware Specification | No | The paper mentions training 'in a single processor' but does not provide specific details about the hardware used for experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper mentions using 'LIBLINEAR; [12]' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We use linear and kernel SVMs as hash functions. Without loss of generality (see later), we use the Laplacian objective (1), which for a single bit takes the form E(z) = PN n,m=1 ynm(zn zm)2, zn = h(xn) { 1, 1}, n = 1, . . . , N. (2) To optimize it, we use a two-step approach... We train the hash functions in a subset of 5 000 points of the training set... As hash functions (for each bit), we use linear SVMs (trained with LIBLINEAR; [12]) and kernel SVMs (with 500 basis functions centered at a random subset of training points). |