TUCH: Turning Cross-view Hashing into Single-view Hashing via Generative Adversarial Nets
Authors: Xin Zhao, Guiguang Ding, Yuchen Guo, Jungong Han, Yue Gao
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical evidence shows that our TUCH approach achieves state-of-the-art results, especially on text to image retrieval, based on image-sentences datasets, i.e. standard IAPRTC-12 and large-scale Microsoft COCO. |
| Researcher Affiliation | Academia | Tsinghua National Laboratory for Information Science and Technology (TNList) School of Software, Tsinghua University, Beijing 100084, China School of Computing & Communications, Lancaster University, UK |
| Pseudocode | Yes | Algorithm 1 summarizes the training procedure. |
| Open Source Code | No | The paper mentions using the 'open-source Torch7 framework' but does not provide a link or explicit statement for their own source code. |
| Open Datasets | Yes | The evaluation is conducted on two benchmark cross-view datasets: Microsoft COCO [Lin et al., 2014] and IAPR TC-12 [Grubinger et al., 2006]. |
| Dataset Splits | Yes | Microsoft COCO ... contains 82783 training images and 40137 validation images... For Microsoft COCO, we randomly select 5000 images with annotations as training set for hashing, 1000 images with annotations as validation set and 1000 images with annotations as query set. For IAPR TC-12, we randomly select 5000 images with annotations as training set for hashing, 1000 images with annotations as validation set and 100 images with annotations per class as query set. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper states 'We implement the TUCH model in the open-source Torch7 framework' but does not provide specific version numbers for Torch7 or any other libraries. |
| Experiment Setup | Yes | The TUCH approach involves 4 penalty parameters λ1, λ2, λH and λG for trading off the relative importance of irrelevant image, fake image, discriminative loss for H and discriminative loss for G in Equ (6) and Equ (7). And we can achieve good results with λ1 = λ2 = 0.5 and λH = λG = 0.01. |