Adversarial Sampling for Active Learning (ASAL) Adversarial Sample Generation using GANs Discriminator 的作用是确保generator 生成的样本与真实样本相差无几。作者的想法是从Generator的输出中进行Uncertainty Sampling,这样uncertainty sampling的两种策略可以表示为: 以及 但是作者同时指出,直接生成样本有两个问题: 需要人...
Adversarial Sampling for Active Learningdoi:10.1109/WACV45572.2020.9093556Christoph MayerRadu TimofteIEEEWorkshop on Applications of Computer Vision
We describe a pool-based semi-supervised active learning algorithm that implicitly learns this sampling mechanism in an adversarial manner. Unlike conventional active learning algorithms, our approach is task agnostic, i.e., it does not depend on the performance of the task for which we are ...
Then, we use the active learning method to train the learning model on the limited samples of the institute. We use the human in the loop to select the sampling for each round of the active learning method. The major motivation of using the input clustering into adversarial samples was to ...
sampling method. 11. IntroductionThe recent success of learning-based computer visionmethods relies heavily on abundant annotated training ex-amples, which may be prohibitively costly to label or im-possible to obtain at large scale [10]. In order to mitigatethis drawback, active learning [4] ...
1. Modality to Modality Translation: An Adversarial Representation Learning and Graph Fusion Network for Multimodal Fusion 会议:AAAI 2020. AAAI Technical Track: AI and the Web. 作者:Sijie Mai, Haifeng Hu, Songlong Xing 链接:https://aaai.org/ojs/index.php/AAAI/article/view/5347/5203 ...
[16] proposed a reservoir sampling method that improves the training efficiency of local models, thereby reducing costs. Li et al. [15] further reduced the number of queries by adopting active learning. However, even with these advances, there are still too many queries required. In addition,...
"People have been using uncertainty for active learning for years in ML potentials. The key difference is that they need to run the full MLsimulationand evaluate if the NN was reliable, and if it wasn't, acquire more data, retrain and re-simulate. Meaning that it takes a long time to ...
self-taught learning Balancing exploitation and exploration 作者尝试结合 GAAL+Random sampling,并在手写体数据集上做实验(5-7,MNIST for training,USPS for test)。实验结果表明:混合方法的效果优于单独使用的方法。A mixed scheme is able to achieve better performance than either using GAAL or random ...
learning environments. Consequently, image statistics learned by neural nets are likely deficient. Bhojanapalli et al.49and Sun et al.50found that as training corpus size increases, neural networks do show improved robustness to adversarial attacks; this robustness is observed for both convolutional ...