Not All Out-of-Distribution Data Are Harmful to Open-Set Active Learning
Authors: Yang Yang, Yuxuan Zhang, XIN SONG, Yi Xu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various open-set AL scenarios demonstrate the effectiveness of the proposed PAL, compared with the state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Yang Yang1, Yuxuan Zhang1, Xin Song2, Yi Xu3 1Nanjing University of Science and Technology 2Baidu Talent Intelligence Center, Baidu Inc 3Dalian University of Technology {yyang, xuan_zhang}@njust.edu.cn, songxin06@baidu.com, yxu@dlut.edu.cn |
| Pseudocode | No | The paper includes a framework diagram (Figure 2) but no explicitly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | The code is available at https://github.com/njustkmg/PAL. |
| Open Datasets | Yes | We evaluate the efficiency of PAL on several image classification benchmarks, i.e., CIFAR10, CIFAR-100 [13] and Tiny-Imagenet [36] datasets following standard open-set AL methods [12, 22]. |
| Dataset Splits | Yes | The CIFAR-10 dataset consists of 50,000 training images and 10,000 test images... The CIFAR-100 dataset has 100 classes and 50,000 training images and 5,000 test images... The Tiny-Imagenet dataset consists of 100,000 training images and 10,000 validation images... For all AL methods, following [22], we randomly sample 1%, 10% and 10% of the examples as the initial labeled set on CIFAR-10, CIFAR-100, and Tiny-Imagenet datasets, respectively. |
| Hardware Specification | Yes | All experiments are implemented on a single NVIDIA V100 GPU. |
| Software Dependencies | No | The paper mentions using Wide Res Net as backbone and SGD optimizer, but does not provide specific version numbers for programming languages or deep learning frameworks. |
| Experiment Setup | Yes | In each AL round, we train the model for 100 epochs, using the SGD optimizer with the momentum parameter of 0.9. The learning rate is initialized as 0.01 with a mini-batch size of 128, and the weight decay is set to be 5 10 4. |