Partial Domain Adaptation
19 papers with code • 5 benchmarks • 4 datasets
Partial Domain Adaptation is a transfer learning paradigm, which manages to transfer relevant knowledge from a large-scale source domain to a small-scale target domain.
Source: Deep Residual Correction Network for Partial Domain Adaptation
Libraries
Use these libraries to find Partial Domain Adaptation models and implementations
Office-Home
DomainNet
Office-31
VisDA-2017
Latest papers with no code
A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting
Specifically, the proposed LIWUDA method constructs a weight network to assign weights to each instance based on its probability of belonging to common classes, and designs Weighted Optimal Transport (WOT) for domain alignment by leveraging instance weights.
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.
MOT: Masked Optimal Transport for Partial Domain Adaptation
A novel masked OT (MOT) methodology on conditional distributions is proposed by defining a mask operation with label information.
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.
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods
In this work, we consider the Partial Domain Adaptation (PDA) variant, where we have extra source classes not present in the target domain.
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.
Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation
To enable a better transferability of the trained models between two different domains, particularly in setups where only the healthy data class is shared between the two domains, we propose a new framework for Partial and Open-Partial domain adaptation based on generating distinct fault signatures with a Wasserstein GAN.
Low-Cost On-device Partial Domain Adaptation (LoCO-PDA): Enabling efficient CNN retraining on edge devices
Consequently, it is likely that the observed data distribution upon deployment is a subset of the training data distribution.
Self-Adaptive Partial Domain Adaptation
Partial Domain adaptation (PDA) aims to solve a more practical cross-domain learning problem that assumes target label space is a subset of source label space.
