【Active Learning - 05】Adversarial Sampling for Active Learning ADVERSARIAL SAMPLING FOR ACTIVE LEARNING 阅读时间: 2019.01.02:早上-选择要精读这篇论文;晚上-abstract、Introduction 部分; 2019.05.01:To be continued… 论文: https://arxiv.org/pdf/1808.06671.pdf 作者(PHD)信息:ETH Zurich, Sw......
Another benefit of ASAL is its small run-time complexity (sub-linear) compared to traditional uncertainty sampling (linear). We present a comprehensive set of experiments on multiple traditional data sets and show that ASAL outperforms similar methods and clearly exceeds the established baseline (...
By sampling from a generative model, you’re able to generate new data. While discriminative models are used for supervised learning, generative models are often used with unlabeled datasets and can be seen as a form of unsupervised learning. Using the dataset of handwritten digits, you could ...
Active learning aims to develop label-efficient algorithms by sampling the most representative queries to be labeled by an oracle. We describe a pool-based semi-supervised active learning algorithm that implicitly learns this sampling mechanism in an adversarial manner. Unlike conventional active learning...
From the perspective of online learning, the adoption of an exploration strategy would also affect the collecting of training data, which further influences model learning. 现有的方法主要是UCB [1], Thompson Sampling (TS) [2] 这种类型的方法,它们用uncertainty来描述一个item的潜在收益。(因此就是将...
It typically includes techniques such as: re-weighting and sampling techniques. A joint end-to-end framework for learning with noisy labels integrates noise handling directly into the model training process. This approach allows the model to simultaneously learn from both clean and noisy labels while...
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: Advances in neural information processing systems. pp. 3630–3638 (2016) Google Scholar Vitter, J.S.: Random sampling with a reservoir. ACM Trans. Math. Softw. (TOMS)11(...
When combined with an active learning loop, this approach bootstraps and improves NN potentials while decreasing the number of calls to the ground truth method. This efficiency is demonstrated on sampling of kinetic barriers, collective variables in molecules, and supramolecular chemistry in zeolite-...
It is originated from the traditional CNNs and reduces the number of parameters of CNNs by downsampling and summarizing from the representations, which makes the training process highly efficient. Similarly, some studies try to generalize pooling operations to graphs for extracting effective information...
Adversarial Sampling for Active Learning ICLR 2019 · Christoph Mayer, Radu Timofte · Edit social preview This paper proposes asal, a new GAN based active learning method that generates high entropy samples. I