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Found 7 papers, 0 papers with code
Robust Class-Conditional Distribution Alignment for Partial Domain Adaptation
Unwanted samples from private source categories in the learning objective of a partial domain adaptation setup can lead to negative transfer and reduce classification performance.
A Robust Negative Learning Approach to Partial Domain Adaptation Using Source Prototypes
This work proposes a robust Partial Domain Adaptation (PDA) framework that mitigates the negative transfer problem by incorporating a robust target-supervision strategy.
Domain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptation
The standard closed-set domain adaptation approaches seek to mitigate distribution discrepancies between two domains under the constraint of both sharing identical label sets.
Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptation
In contrast to a standard closed-set domain adaptation task, partial domain adaptation setup caters to a realistic scenario by relaxing the identical label set assumption.
Partial Domain Adaptation Using Selective Representation Learning For Class-Weight Computation
Driven by the motivation that image styles are private to each domain, in this work, we develop a method that identifies outlier classes exclusively from image content information and train a label classifier exclusively on class-content from source images.
Predicting Future Opioid Incidences Today
According to the Center of Disease Control (CDC), the Opioid epidemic has claimed more than 72, 000 lives in the US in 2017 alone.
Combining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling
Modern deep learning algorithms have triggered various image segmentation approaches.
