Variational Interaction Information Maximization for Cross-domain Disentanglement
Authors: HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the validity of our model in the image-to-image translation and the cross-domain retrieval tasks. We further show that our model achieves the state-of-the-art performance in the zero-shot sketch based image retrieval task, even without external knowledge. 4 Experiments We employ experiments on image-to-image translation and image retrieval tasks to evaluate the quality of cross-domain disentanglement. |
| Researcher Affiliation | Academia | Hyeong Joo Hwang1, Geon-Hyeong Kim2, Seunghoon Hong2, Kee-Eung Kim1,2 1 Graduate School of AI, KAIST, Daejeon, Republic of Korea 2 School of Computing, KAIST, Daejeon, Republic of Korea |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | We leave all the implementation details and hyperparameter settings in D in the supplementary material. |
| Open Datasets | Yes | Datasets We evaluate our method on two datasets: MNIST-CDCB [12] and Cars [36] datasets. ... We tested our model with MNIST-CDCB [12], Facades [41], and Maps [17] datasets. ... We evaluate our model on Sketchy (Extended) [29, 37], one of the most widely used datasets of sketch and photo images in sketch-based image retrieval (SBIR) task. |
| Dataset Splits | Yes | In MNIST-CDCB [12] dataset... We use 50,000 / 10,000 pairs of train/test samples following [24]. Cars [36]... Out of those 16,836 pairs of 183 cars, we assigned 16,192 pairs of 176 cars to train set and 644 pairs of 7 cars to test set. ... In Facades [41] dataset... We use 400 / 100 / 106 pairs of train/valid/test samples following [41]. In Maps [17] dataset... We use 1096 / 1098 pairs of train/test samples following [17]. |
| Hardware Specification | No | The paper does not provide specific hardware details for running experiments. |
| Software Dependencies | No | extracted features of images from VGG16 and finetuned with the train set of Sketchy Extended. |
| Experiment Setup | No | We leave all the implementation details and hyperparameter settings in D in the supplementary material. |