EDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap Expansion
Authors: Yuchen Sun, Qianqian Xu, Zitai Wang, Zhiyong Yang, Junwei He
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, comprehensive experimental results on multiple multi-label and OOD datasets reveal the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | 1Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, CAS, Beijing, China 2School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Energy Distribution Gap Expansion (EDGE) Input: ID training set Dtrain in, OE dataset DOE; Parameter: model parameter θ, epoch T, transform epoch τ, hyper-parameter α, β, k, m; 1: Compute dilation distance between Dtrain in and each individual set DOE by Eq. (9). 2: for t = 0, 1, . . . , T do 3: Sample a batch of ID and OE training data from Dtrain in and DOE respectively. 4: if t < τ then 5: Set β = 0. 6: end if 7: Calculate each loss using Eq. (4), (5), and (6). 8: Update parameter θ by minimizing (8). 9: end for |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating the release of source code for the methodology described. |
| Open Datasets | Yes | We adopt three real-world benchmark multi-label image annotation datasets as our training sets, i.e., PASCAL-VOC (Everingham et al. 2015), MSCOCO (Lin et al. 2014) and NUS-WIDE (Chua et al. 2009). ... we choose the following benchmark OOD datasets as main experimental candidates: (a subset of) Image Net-22K (Deng et al. 2009), Textures (Cimpoi et al. 2014), Places50 (Zhou et al. 2018), i SUN (Xu et al. 2015), i Naturalist (Horn et al. 2018), LSUN-C (Yu et al. 2015), and SVHN (Netzer et al. 2011). |
| Dataset Splits | Yes | MS-COCO contains 122,218 annotation images with 80 categories, where 82,783 images are for training, 40,504 for validation, and 40,775 for testing; and NUS-WIDE covers 269,648 real-world images with 81 categories, where we reserve 119,986 images of the training set and 80,283 images of the test set, respectively. |
| Hardware Specification | Yes | All the experiments are run on NVIDIA Ge Force RTX 3090 implemented by Py Torch and conducted on the Res Net-50 (He et al. 2016) and VGG16 (Simonyan and Zisserman 2015) pre-trained on Image Net-1K (Deng et al. 2009). |
| Software Dependencies | No | All the experiments are run on NVIDIA Ge Force RTX 3090 implemented by Py Torch and conducted on the Res Net-50 (He et al. 2016) and VGG16 (Simonyan and Zisserman 2015) pre-trained on Image Net-1K (Deng et al. 2009). We use stochastic gradient descent (SGD) (Sutskever et al. 2013) with a momentum of 0.9 and a weight decay of 1e-4. For trivial BCE models, we use the Adam (Kingma and Ba 2015) to optimize. |
| Experiment Setup | Yes | We use stochastic gradient descent (SGD) (Sutskever et al. 2013) with a momentum of 0.9 and a weight decay of 1e-4. For trivial BCE models, we use the Adam (Kingma and Ba 2015) to optimize. The main hyper-parameters α, β, and m take value from {1e + 1, 1, 1e 1, 1e 2, 1e 3}, {1, 1e 1, 1e 2, 1e 3, 1e 4}, and {0, 1, 2, 3, 4, 5}. |