Few-Shot Partial-Label Learning
Authors: Yunfeng Zhao, Guoxian Yu, Lei Liu, Zhongmin Yan, Lizhen Cui, Carlotta Domeniconi
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on widely-used few-shot datasets demonstrate that our Fs PLL can achieve a superior performance than the state-of-the-art methods, and it needs fewer samples for quickly adapting to new tasks. Extensive experiments on benchmark few-shot datasets show that our Fs PLL outperforms the state-of-the-art PLL approaches [Zhang et al., 2016; Wu and Zhang, 2018; Feng and An, 2019; Wang et al., 2019] and baseline FSL methods [Snell et al., 2017; Finn et al., 2017]. |
| Researcher Affiliation | Academia | Yunfeng Zhao1,2 , Guoxian Yu1,2 , Lei Liu1,2 , Zhongmin Yan1,2 , Lizhen Cui1,2 , Carlotta Domeniconi3 1School of Software Engineering, Shandong University, Jinan, Shandong, China 2Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, China 3Department of Computer Science, George Mason University, VA, USA |
| Pseudocode | No | The paper does not contain a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link for open-source code for the methodology described. |
| Open Datasets | Yes | We conduct experiments on two benchmark FSL datasets (Omniglot [Lake et al., 2011] and mini Image Net [Vinyals et al., 2016]). |
| Dataset Splits | Yes | Each Dt train consisted of N1 = 30 classes were randomly sampled from 4800/80 train classes of Omniglot/mini Image Net without replacement. As to the meta-testing set, we randomly selected another N2 classes from 1692/20 test classes without replacement. For each selected class, K1 = 5 (K2) samples were randomly chosen from 20/600 samples without replacement for the meta-training (meta-testing) support samples, and the remaining/15 samples per class were randomly chosen as the query samples. They only use the samples in meta-testing set for training and validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions the Adam optimizer but does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | As to our Fs PLL, the trade-off parameter λ is fixed as 0.5 (0 for Fs PLLn M), the number of nearest neighbors k = K2 1, the number of iterations for prototype rectification in each epoch is fixed to 10. In addition, we use the Adam [Kingma and Ba, 2015] optimizer, the learning rate is fixed as 0.001 and cut into half per 20 epochs. |