# Domain Adaptation by Mixture of Alignments of Second- or Higher-Order Scatter Tensors

arXiv: https://arxiv.org/abs/1611.08195 · HTML: https://mlsanity.com/paper/1611.08195/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2016.

## Built on by (most cited first, 18 of 18)

- [DINE: Domain Adaptation from Single and Multiple Black-box Predictors](https://mlsanity.com/paper/2104.01539/) (2021)
- [Meta-Learning for Multi-Label Few-Shot Classification](https://mlsanity.com/paper/2110.13494/) (2021)
- [Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors](https://mlsanity.com/paper/2205.14467/) (2022)
- [Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts](https://mlsanity.com/paper/2106.00322/) (2021)
- [High-order Tensor Pooling with Attention for Action Recognition](https://mlsanity.com/paper/2110.05216/) (2021)
- [Multi-level Second-order Few-shot Learning](https://mlsanity.com/paper/2201.05916/) (2022)
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- [Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment](https://mlsanity.com/paper/2402.04599/) (2024)
- [From Big to Small: Adaptive Learning to Partial-Set Domains](https://mlsanity.com/paper/2203.07375/) (2022)
- [Egocentric zone-aware action recognition across environments](https://mlsanity.com/paper/2409.14205/) (2024)
- [Simple Dialogue System with AUDITED](https://mlsanity.com/paper/2110.11881/) (2021)
- [Multivariate Prototype Representation for Domain-Generalized Incremental Learning](https://mlsanity.com/paper/2309.13563/) (2023)
- [SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities](https://mlsanity.com/paper/2407.11676/) (2024)
- [Learning Tensor Representations for Meta-Learning](https://mlsanity.com/paper/2201.07348/) (2022)
- [Open-Set Multi-Source Multi-Target Domain Adaptation](https://mlsanity.com/paper/2302.00995/) (2023)
- [SADA: Semantic adversarial unsupervised domain adaptation for Temporal Action Localization](https://mlsanity.com/paper/2312.13377/) (2023)
- [An Investigation of Test-time Adaptation for Audio Classification under Background Noise](https://mlsanity.com/paper/2507.15523/) (2025)
- [ALSA: Anchors in Logit Space for Out-of-Distribution Accuracy Estimation](https://mlsanity.com/paper/2508.19613/) (2025)

## Abstract (arXiv)

In this paper, we propose an approach to the domain adaptation, dubbed Second- or Higher-order Transfer of Knowledge (So-HoT), based on the mixture of alignments of second- or higher-order scatter statistics between the source and target domains. The human ability to learn from few labeled samples is a recurring motivation in the literature for domain adaptation. Towards this end, we investigate the supervised target scenario for which few labeled target training samples per category exist. Specifically, we utilize two CNN streams: the source and target networks fused at the classifier level. Features from the fully connected layers fc7 of each network are used to compute second- or even higher-order scatter tensors; one per network stream per class. As the source and target distributions are somewhat different despite being related, we align the scatters of the two network streams of the same class (within-class scatters) to a desired degree with our bespoke loss while maintaining good separation of the between-class scatters. We train the entire network in end-to-end fashion. We provide evaluations on the standard Office benchmark (visual domains), RGB-D combined with Caltech256 (depth-to-rgb transfer) and Pascal VOC2007 combined with the TU Berlin dataset (image-to-sketch transfer). We attain state-of-the-art results.