Direct Hashing Without Pseudo-Labels
Authors: Feng Zheng, Heng Huang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results demonstrate that the retrieval performance both in uni-modal and cross-modal settings can be improved. To validate the proposed framework, we compare it with the state-of-the-art methods in five datasets: toy set, SIFT1M (Jegou, Douze, and Schmid 2011) CIFAR-10 (Krizhevsky and Hinton 2009), MNIST2 and VIPe R (Gray and Tao 2008). |
| Researcher Affiliation | Academia | Feng Zheng, Heng Huang Electrical and Computer Engineering, University of Pittsburgh 3700 O Hara Street, Pittsburgh, PA, USA 15261 {feng.zheng, heng.huang}@pitt.edu |
| Pseudocode | Yes | Algorithm 1 WSL Hashing Input: Training dataset X and parameters: μ, ν and K. Output: F(x) = (w1, , w K)T x. Initialisation: Randomly initiate w0 1, , w0 K. Construct S for X and calculate matrix L. |
| Open Source Code | No | The paper does not provide any links to source code or explicitly state that code is made available. |
| Open Datasets | Yes | We compare it with the state-of-the-art methods in five datasets: toy set, SIFT1M (Jegou, Douze, and Schmid 2011) CIFAR-10 (Krizhevsky and Hinton 2009), MNIST2 and VIPe R (Gray and Tao 2008). 2http://yann.lecun.com/exdb/mnist/. |
| Dataset Splits | No | The paper mentions a train/test split for VIPeR and refers to an external paper for CIFAR-10 and MNIST settings, but it does not explicitly provide validation dataset splits within the paper itself. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, memory, or specific computer specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions optimizers like MMA and others, but it does not provide specific software dependencies or library versions (e.g., Python version, PyTorch version, etc.) needed to replicate the experiments. |
| Experiment Setup | Yes | The parameters of the proposed model are set as μ = 0.05 and ν = 0.6. |