LPS uses a second-order neural network to learn an end-to-end metric for second-order parameter similarity and introduces the ModelSet500 benchmark with 500 trained models.
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
abstract
Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts. Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at mapping images between domains, even without the use of aligned image pairs. We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model. CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs. Our model can be applied in a variety of visual recognition and prediction settings. We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.
years
2019 4verdicts
UNVERDICTED 4representative citing papers
MMI-ALI extends pairwise ALI models into an m-domain ensemble by maximizing MMI on joint variables to achieve scalable joint distribution matching with linear scaling in m.
CovNorm reduces parameters in domain-adaptive layers via two PCAs and a mini-adaptation layer, enabling efficient multi-domain learning with performance close to full fine-tuning.
Synthetic data can partially substitute for real data in object detection training, with performance tied to domain similarity and the volume of real data included.
citing papers explorer
-
Learnable Parameter Similarity
LPS uses a second-order neural network to learn an end-to-end metric for second-order parameter similarity and introduces the ModelSet500 benchmark with 500 trained models.
-
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
MMI-ALI extends pairwise ALI models into an m-domain ensemble by maximizing MMI on joint variables to achieve scalable joint distribution matching with linear scaling in m.
-
Efficient Multi-Domain Network Learning by Covariance Normalization
CovNorm reduces parameters in domain-adaptive layers via two PCAs and a mini-adaptation layer, enabling efficient multi-domain learning with performance close to full fine-tuning.
-
How much real data do we actually need: Analyzing object detection performance using synthetic and real data
Synthetic data can partially substitute for real data in object detection training, with performance tied to domain similarity and the volume of real data included.