Domain Generalization
623 papers with code • 19 benchmarks • 25 datasets
The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain
Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
Libraries
Use these libraries to find Domain Generalization models and implementationsDatasets
Latest papers
Generative Medical Segmentation
Concretely, GMS employs a robust pre-trained Variational Autoencoder (VAE) to derive latent representations of both images and masks, followed by a mapping model that learns the transition from image to mask in the latent space.
MatchSeg: Towards Better Segmentation via Reference Image Matching
Few-shot learning aims to overcome the need for annotated data by using a small labeled dataset, known as a support set, to guide predicting labels for new, unlabeled images, known as the query set.
DomainLab: A modular Python package for domain generalization in deep learning
DomainLab is a modular Python package for training user specified neural networks with composable regularization loss terms.
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
When a neural network parameterized loss function consists of many terms, the combinatorial choice of weight multipliers during the optimization process forms a challenging problem.
Negative Yields Positive: Unified Dual-Path Adapter for Vision-Language Models
Recently, large-scale pre-trained Vision-Language Models (VLMs) have demonstrated great potential in learning open-world visual representations, and exhibit remarkable performance across a wide range of downstream tasks through efficient fine-tuning.
Towards Generalizing to Unseen Domains with Few Labels
Existing domain generalization (DG) methods which are unable to exploit unlabeled data perform poorly compared to semi-supervised learning (SSL) methods under SSDG setting.
Neural Markov Random Field for Stereo Matching
Stereo matching is a core task for many computer vision and robotics applications.
A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimation
Furthermore, the pose estimator's optimization is not exposed to domain shifts, limiting its overall generalization ability.
Single Domain Generalization for Crowd Counting
We propose MPCount, a novel SDG approach effective even for narrow source distribution.
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
Conventional wisdom suggests parameter-efficient fine-tuning of foundation models as the state-of-the-art method for transfer learning in vision, replacing the rich literature of alternatives such as meta-learning.