Predictive Adversarial Learning from Positive and Unlabeled Data
Authors: Wenpeng Hu, Ran Le, Bing Liu, Feng Ji, Jinwen Ma, Dongyan Zhao, Rui Yan7806-7814
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluation using both image and text data shows that PAN outperforms state-of-the-art PU learning methods and also a direct adaptation of GAN for PU learning. |
| Researcher Affiliation | Collaboration | Wenpeng Hu1,*, Ran Le2,*, Bing Liu3, , Feng Ji4, Jinwen Ma1, Dongyan Zhao2, Rui Yan2, 1 Department of Information Science, School of Mathematical Sciences, Peking University 2 Wangxuan Institute of Computer Technology, Peking University 3 Department of Computer Science, University of Illinois at Chicago 4 Alibaba Group {wenpeng.hu, leran, jwma, zhaody, ruiyan}@pku.edu.cn, liub@uic.edu, zhongxiu.jf@alibaba-inc.com |
| Pseudocode | Yes | Algorithm 1 PAN training by the minibatch stochastic gradient descent method. |
| Open Source Code | No | The paper does not provide concrete access to source code for the PAN methodology described in this paper. It only references open-source code for baselines. |
| Open Datasets | Yes | YELP: http://www.yelp.com/dataset challenge; RT: http://www.cs.cornell.edu/ people/pabo/movie-review-data/; IMDB: https://www.imdb.com/interfaces/; 20NEWS: http://qwone.com/ jason/20Newsgroups/; MNIST: http://yann.lecun.com/ exdb/mnist/; CIFAR10: http://www.cs.toronto.edu/ kriz/cifar10-python.tar.gz . |
| Dataset Splits | No | The paper describes how training data (Positive P and Unlabeled U) and test data are prepared, but does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments, only general training configurations. |
| Software Dependencies | No | The paper mentions using 'Adam algorithm' for optimization and 'tensorflow' for a baseline, but does not specify version numbers for programming languages, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | Training Details: For a fair comparison, PAN uses the same architecture for classifier C( ) as NNPU. For text, a 2-layer convolutional network (CNN), with 5 * 100 and 3 * 100 convolutions for layers 1 and 2 respectively, and 100 filters for each layer, is used as the classifier C( ) and discriminator D( ). (...) We set λ in Eq. 3 and Eq. 7 to 0.0001, (...) We also balance the impact of positive and unlabeled data for term I in Eq. 3 in training; otherwise the positive examples will be dominated by the unlabeled data. We use 1:1 ratio of positive data and unlabeled data in each mini-batch in training. The network parameters are updated using the Adam algorithm with learning rate 0.0001. For a-GAN, it needs pre-training of D( ). We use the original positive and unlabeled (regarded as negative) data to pre-train D( ) in order to give it the ability to classify positive and unlabeled data. We pre-train D( ) 3 epochs for each dataset. |