U-CAM uses gradients of aleatoric and predictive uncertainty losses to sharpen visual attention maps and improve VQA accuracy over standard baselines.
Looking back at Labels: A Class based Domain Adaptation Technique
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
In this paper, we solve the problem of adapting classifiers across domains. We consider the problem of domain adaptation for multi-class classification where we are provided a labeled set of examples in a source dataset and we are provided a target dataset with no supervision. In this setting, we propose an adversarial discriminator based approach. While the approach based on adversarial discriminator has been previously proposed; in this paper, we present an informed adversarial discriminator. Our observation relies on the analysis that shows that if the discriminator has access to all the information available including the class structure present in the source dataset, then it can guide the transformation of features of the target set of classes to a more structure adapted space. Using this formulation, we obtain state-of-the-art results for the standard evaluation on benchmark datasets. We further provide detailed analysis which shows that using all the labeled information results in an improved domain adaptation.
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2019 1verdicts
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U-CAM: Visual Explanation using Uncertainty based Class Activation Maps
U-CAM uses gradients of aleatoric and predictive uncertainty losses to sharpen visual attention maps and improve VQA accuracy over standard baselines.