# Robust Internal Representations for Domain Generalization

arXiv: https://arxiv.org/abs/2309.15522 · HTML: https://mlsanity.com/paper/2309.15522/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 65 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 30 of 32)

- [CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks](https://mlsanity.com/paper/2206.09059/) (2022)
- [Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay](https://mlsanity.com/paper/1903.04566/) (2019)
- [Unsupervised Model Adaptation for Continual Semantic Segmentation](https://mlsanity.com/paper/2009.12518/) (2020)
- [Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning](https://mlsanity.com/paper/2104.08808/) (2021)
- [Detection and Continual Learning of Novel Face Presentation Attacks](https://mlsanity.com/paper/2108.12081/) (2021)
- [Generative Continual Concept Learning](https://mlsanity.com/paper/1906.03744/) (2019)
- [Overcoming Concept Shift in Domain-Aware Settings through Consolidated Internal Distributions](https://mlsanity.com/paper/2007.00197/) (2020)
- [Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer](https://mlsanity.com/paper/1710.03850/) (2017)
- [Cognitively Inspired Learning of Incremental Drifting Concepts](https://mlsanity.com/paper/2110.04662/) (2021)
- [One-shot Learning for Temporal Knowledge Graphs](https://mlsanity.com/paper/2010.12144/) (2020)
- [Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioning](https://mlsanity.com/paper/2303.12424/) (2023)
- [Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition](https://mlsanity.com/paper/1709.05412/) (2017)
- [Unsupervised Model Adaptation for Source-free Segmentation of Medical Images](https://mlsanity.com/paper/2211.00807/) (2022)
- [Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment](https://mlsanity.com/paper/1907.02271/) (2019)
- [Domain Adaptation for the Segmentation of Confidential Medical Images](https://mlsanity.com/paper/2101.00522/) (2021)
- [Secure Domain Adaptation with Multiple Sources](https://mlsanity.com/paper/2106.12124/) (2021)
- [Task-Attentive Transformer Architecture for Continual Learning of Vision-and-Language Tasks Using Knowledge Distillation](https://mlsanity.com/paper/2303.14423/) (2023)
- [Zero-Shot Image Classification Using Coupled Dictionary Embedding](https://mlsanity.com/paper/1906.10509/) (2019)
- [Joint Dictionaries for Zero-Shot Learning](https://mlsanity.com/paper/1709.03688/) (2017)
- [Identification of Novel Classes for Improving Few-Shot Object Detection](https://mlsanity.com/paper/2303.10422/) (2023)
- [I2I: Initializing Adapters with Improvised Knowledge](https://mlsanity.com/paper/2304.02168/) (2023)
- [Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks](https://mlsanity.com/paper/1812.00265/) (2018)
- [History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2305.18675/) (2023)
- [Improved Region Proposal Network for Enhanced Few-Shot Object Detection](https://mlsanity.com/paper/2308.07535/) (2023)
- [Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation](https://mlsanity.com/paper/2107.01598/) (2021)
- [Testing fine-grained parallelism for the ADMM on a factor-graph](https://mlsanity.com/paper/1603.02526/) (2016)
- [Preserving Fairness in AI under Domain Shift](https://mlsanity.com/paper/2301.12369/) (2023)
- [Automating Detection of Papilledema in Pediatric Fundus Images with Explainable Machine Learning](https://mlsanity.com/paper/2207.04565/) (2022)
- [Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data](https://mlsanity.com/paper/2301.12361/) (2023)
- [Encoding Binary Concepts in the Latent Space of Generative Models for Enhancing Data Representation](https://mlsanity.com/paper/2303.12255/) (2023)

## Abstract (arXiv)

This paper which is part of the New Faculty Highlights Invited Speaker Program of AAAI'23, serves as a comprehensive survey of my research in transfer learning by utilizing embedding spaces. The work reviewed in this paper specifically revolves around the inherent challenges associated with continual learning and limited availability of labeled data. By providing an overview of my past and ongoing contributions, this paper aims to present a holistic understanding of my research, paving the way for future explorations and advancements in the field. My research delves into the various settings of transfer learning, including, few-shot learning, zero-shot learning, continual learning, domain adaptation, and distributed learning. I hope this survey provides a forward-looking perspective for researchers who would like to focus on similar research directions.