# Selective Prediction Forgetting for Adaptation of Weakly Supervised Localization of Histology Images

arXiv: https://arxiv.org/abs/2603.12468 · HTML: https://mlsanity.com/paper/2603.12468/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 29 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Builds on (most cited first, 22 of 22)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020)
- [TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization](https://mlsanity.com/paper/2103.14862/) (2021)
- [A Comprehensive Survey on Source-free Domain Adaptation](https://mlsanity.com/paper/2302.11803/) (2023)
- [Source-Free Domain Adaptation via Distribution Estimation](https://mlsanity.com/paper/2204.11257/) (2022)
- [Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time](https://mlsanity.com/paper/2211.14238/) (2022)
- [Source-Free Unsupervised Domain Adaptation: A Survey](https://mlsanity.com/paper/2301.00265/) (2022)
- [Cross-domain Contrastive Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2106.05528/) (2021)
- [When Source-Free Domain Adaptation Meets Learning with Noisy Labels](https://mlsanity.com/paper/2301.13381/) (2023)
- [Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey](https://mlsanity.com/paper/1909.03354/) (2019)
- [F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling](https://mlsanity.com/paper/2109.07069/) (2021)
- [Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology](https://mlsanity.com/paper/2404.19113/) (2024)
- [Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data](https://mlsanity.com/paper/2503.20771/) (2025)
- [DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization](https://mlsanity.com/paper/2310.06196/) (2023)
- [Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method](https://mlsanity.com/paper/2508.09202/) (2025)
- [Approximate Domain Unlearning for Vision-Language Models](https://mlsanity.com/paper/2510.08132/) (2025)
- [Spatial-Aware Token for Weakly Supervised Object Localization](https://mlsanity.com/paper/2303.10438/) (2023)
- [MuSACo: Multimodal Subject-Specific Selection and Adaptation for Expression Recognition with Co-Training](https://mlsanity.com/paper/2508.12522/) (2025)
- [PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization](https://mlsanity.com/paper/2503.24135/) (2025)
- [Low-Rank Expert Merging for Multi-Source Domain Adaptation in Person Re-Identification](https://mlsanity.com/paper/2508.06831/) (2025)
- [CLIP-IT: CLIP-based Pairing for Histology Images Classification](https://mlsanity.com/paper/2504.16181/) (2025)

## Abstract (arXiv)

Under strong cross-domain shifts, weakly supervised object localization (WSOL) models often lose their capacity to discriminate and develop a strong prediction bias toward a subset of classes. As a result, the majority of target samples are assigned to these dominant classes. Since source-free (unsupervised) domain adaptation (SFDA) methods typically rely on pseudo-labels, inaccurate pseudo-label estimation causes suboptimal adaptation and poor classification and localization performance. This issue is particularly challenging in computational histopathology, where shifts in organ-specific morphology, staining protocols, and acquisition conditions can significantly impact class-discriminative visual cues. This paper introduces SFDA with selective prediction forgetting (SFDA-SPF), a method designed to mitigate excessive concentration of predictions for dominant classes during target-domain adaptation. SFDA-SPF identifies prediction-dominant classes based on the model's target predictions and selectively revises uncertain samples assigned to them. Importantly, this procedure does not require knowledge of the true target class distribution. In contrast with previous SFDA methods, SFDA-SPF introduces a 'forget set' composed of uncertain samples from dominant classes near their decision boundaries, enabling the classification boundaries to shift away from biased predictions. A second 'retain set' is used to anchor certain and accurate samples. To forget the predictions of dominant classes, SFDA-SPF introduces a complementary optimization objective that reduces their probability while allowing alternative class assignments to emerge. This iterative optimization process reassesses target images for forget and retain sets at regular intervals. In addition to improving image-level pseudo-label estimation, the pixel-level classifier is jointly optimized, further enhancing localization.