# Attending to Discriminative Certainty for Domain Adaptation

arXiv: https://arxiv.org/abs/1906.03502 · HTML: https://mlsanity.com/paper/1906.03502/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2019.

## Built on by (most cited first, 30 of 37)

- [CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2109.06165/) (2021)
- [Unsupervised Domain Adaptation of Object Detectors: A Survey](https://mlsanity.com/paper/2105.13502/) (2021)
- [Domain Impression: A Source Data Free Domain Adaptation Method](https://mlsanity.com/paper/2102.09003/) (2021)
- [Context-Aware Mixup for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2108.03557/) (2021)
- [Uncertainty-guided Source-free Domain Adaptation](https://mlsanity.com/paper/2208.07591/) (2022)
- [Transporting Causal Mechanisms for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2107.11055/) (2021)
- [ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2106.10812/) (2021)
- [Transformer-Based Source-Free Domain Adaptation](https://mlsanity.com/paper/2105.14138/) (2021)
- [Leveraging Self-Supervision for Cross-Domain Crowd Counting](https://mlsanity.com/paper/2103.16291/) (2021)
- [DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2207.09988/) (2022)
- [CoNMix for Source-free Single and Multi-target Domain Adaptation](https://mlsanity.com/paper/2211.03876/) (2022)
- [Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement](https://mlsanity.com/paper/2201.01929/) (2022)
- [Do Not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting](https://mlsanity.com/paper/2102.01906/) (2021)
- [Robust Local Preserving and Global Aligning Network for Adversarial Domain Adaptation](https://mlsanity.com/paper/2203.04156/) (2022)
- [Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling](https://mlsanity.com/paper/2206.01319/) (2022)
- [ConDA: Continual Unsupervised Domain Adaptation](https://mlsanity.com/paper/2103.11056/) (2021)
- [On Evolving Attention Towards Domain Adaptation](https://mlsanity.com/paper/2103.13561/) (2021)
- [Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints](https://mlsanity.com/paper/2107.01248/) (2021)
- [Reciprocal Normalization for Domain Adaptation](https://mlsanity.com/paper/2112.10474/) (2021)
- [Mitigating Uncertainty of Classifier for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2107.00727/) (2021)
- [Sensor-invariant Fingerprint ROI Segmentation Using Recurrent Adversarial Learning](https://mlsanity.com/paper/2107.01361/) (2021)
- [Gradient Based Activations for Accurate Bias-Free Learning](https://mlsanity.com/paper/2202.10943/) (2022)
- [A Class-aware Optimal Transport Approach with Higher-Order Moment Matching for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2401.15952/) (2024)
- [Semi-Supervised Domain Adaptation via Selective Pseudo Labeling and Progressive Self-Training](https://mlsanity.com/paper/2104.00319/) (2021)
- [Exploring Dropout Discriminator for Domain Adaptation](https://mlsanity.com/paper/2107.04231/) (2021)
- [Explainable Supervised Domain Adaptation](https://mlsanity.com/paper/2205.09943/) (2022)
- [Increasing Model Generalizability for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2209.14644/) (2022)
- [DiTTO: A Feature Representation Imitation Approach for Improving Cross-Lingual Transfer](https://mlsanity.com/paper/2303.02357/) (2023)
- [Domain-Adaptive Learning: Unsupervised Adaptation for Histology Images with Improved Loss Function Combination](https://mlsanity.com/paper/2309.17172/) (2023)
- [Adapt Anything: Tailor Any Image Classifiers across Domains And Categories Using Text-to-Image Diffusion Models](https://mlsanity.com/paper/2310.16573/) (2023)

## Abstract (arXiv)

In this paper, we aim to solve for unsupervised domain adaptation of classifiers where we have access to label information for the source domain while these are not available for a target domain. While various methods have been proposed for solving these including adversarial discriminator based methods, most approaches have focused on the entire image based domain adaptation. In an image, there would be regions that can be adapted better, for instance, the foreground object may be similar in nature. To obtain such regions, we propose methods that consider the probabilistic certainty estimate of various regions and specify focus on these during classification for adaptation. We observe that just by incorporating the probabilistic certainty of the discriminator while training the classifier, we are able to obtain state of the art results on various datasets as compared against all the recent methods. We provide a thorough empirical analysis of the method by providing ablation analysis, statistical significance test, and visualization of the attention maps and t-SNE embeddings. These evaluations convincingly demonstrate the effectiveness of the proposed approach.