Variation Generalized Feature Learning via Intra-view Variation Adaptation
Authors: Jiawei Li, Mang Ye, Andy Jinhua Ma, Pong C Yuen
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on different datasets demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Jiawei Li1 , Mang Ye1 , Andy J Ma2 and Pong C Yuen1 1 Department of Computer Science, Hong Kong Baptist University, Hong Kong 2 School of Data and Computer Science, Sun Yat-sen University, China |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We evaluate our method on three datasets, i.e., the i LIDS-VID dataset [Wang et al., 2014], the PRID 2011 dataset [Hirzer et al., 2011], the MARS dataset [Zheng et al., 2016]. |
| Dataset Splits | Yes | For i LIDS-VID and PRID-2011 datasets, the sequence pairs are randomly separated into half for training and the other half for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using DGM and setting specific parameters but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | 10 iterations are conducted with λ = 0.5 in DGM for all three datasets following [Ye et al., 2017]. We set tmax,1 = 1 for efficiency. λ = 1, λα = 1. The dimension of xdr i,j is set to be the same as xi,j. |