Distribution Matching for Transduction
By Novi Quadrianto, James Petterson, Alex Smola. 2009
48 citations recorded by the source corpus.
Subfields: Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Representation & Self-Supervised Learning
- Tinne Tuytelaars
- Rahaf Aljundi
- Marcus Rohrbach
- Mohamed Elhoseiny
- Viktoriia Sharmanska
- Francesca Babiloni
- Novi Quadrianto
- Joost van de Weijer
Citations in the corpus
- Women also Snowboard: Overcoming Bias in Captioning Models (2018, neutral): “Similar techniques which incorporate distribution matching have been used for other problems including domain adaptation [18,19] and transduction learning [20].”
- Memory Aware Synapses: Learning what (not) to forget (2017, background): “While previous works [26,31] adapt the learning system at prediction time in a transductive setting, our goal here is to build a continual system that can adapt the importance of the weights to what the system needs to remember.”
- Deep Transfer Network: Unsupervised Domain Adaptation (2015, background): “MMD is a commonly used metric of discrepancy between two distributions, due to its efficiency in computation and optimization [26].”
- Memory Aware Synapses: Learning what (not) to forget (2018, background): “While previous works [25,30] adapt the learning system at prediction time in a transductive setting, our goal here is to build a continual system that is able to adapt the importance of the weights to what the system actually needs to remember.”
- Continual Learning in Neural Networks (2019, background): “While previous works [116, 129] adapt the learning system at prediction time in a transductive setting, our goal here is to build a continual system that can adapt the importance of the weights to what the system needs to remember.”
- Posterior Regularization for Structured Latent Variable Models (2010, background): “Closely related to GE, is the work of Quadrianto et al. (2009).”
- Recycling Privileged Learning and Distribution Matching for Fairness (2017, methodology): “However, it was noted in [35] that for an objective function that has an additive structure as in our DM+ model, it is better to use the non-convex objective directly.”