Table of Contents
What is adversarial regularization?
This generalisation of NSL is known as Adversarial Regularisation, where adversarial examples are constructed to intentionally confuse the model during training, resulting in models that are robust against small input perturbations.
Can we use Gan for supervised learning?
The semi-supervised GAN is an extension of the GAN architecture for training a classifier model while making use of labeled and unlabeled data. There are at least three approaches to implementing the supervised and unsupervised discriminator models in Keras used in the semi-supervised GAN.
What is temporal Ensembling?
Temporal ensembling aggregates the outputs of all previous epochs into a collaborative prediction that is expected to be closer to the accurate unknown labels of unannotated inputs. Thus, the labels inferred this way acts as an unsupervised training target to compare against for unlabelled data.
What is stacking ensemble?
Stacking or Stacked Generalization is an ensemble machine learning algorithm. The benefit of stacking is that it can harness the capabilities of a range of well-performing models on a classification or regression task and make predictions that have better performance than any single model in the ensemble.
Which of the following ensemble method works similar to above discussed election procedure?
11 Which of the following ensemble method works similar to above-discussed election procedure? Hint: Persons are like base models of ensemble method. In bagged ensemble, the predictions of the individual models won’t depend on each other. So option A is correct.
When would you use semi-supervised learning?
Additional Resources
- Semi-Supervised Learning Literature Survey, 2005.
- Introduction to Semi-Supervised Learning, 2009.
- An Overview of Deep Semi-Supervised Learning, 2020.
What is the difference between supervised and semi-supervised learning?
Supervised learning aims to learn a function that, given a sample of data and desired outputs, approximates a function that maps inputs to outputs. Semi-supervised learning aims to label unlabeled data points using knowledge learned from a small number of labeled data points.
What is the difference between adversarial and virtual adversarial training?
Unlike adversarial training, our method defines the adversarial direction without label information and is hence applicable to semi-supervised learning. Because the directions in which we smooth the model are only “virtually” adversarial, we call our method virtual adversarial training (VAT). The computational cost of VAT is relatively low.
What is virtual adversarial loss in machine learning?
Virtual adversarial loss is defined as the robustness of the conditional label distribution around each input data point against local perturbation. Unlike adversarial training, our method defines the adversarial direction without label information and is hence applicable to semi-supervised learning.
Is there a new regularization method based on virtual adversarial loss?
Abstract:We propose a new regularization method based on virtual adversarial loss: a new measure of local smoothness of the conditional label distribution given input. Virtual adversarial loss is defined as the robustness of the conditional label distribution around each input data point against local perturbation.
What is the computational cost of virtual adversarial loss (VAT)?
The computational cost of VAT is relatively low. For neural networks, the approximated gradient of virtual adversarial loss can be computed with no more than two pairs of forward- and back-propagations. In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets.