Domain Generalization
635 papers with code • 18 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
Benchmarks
These leaderboards are used to track progress in Domain Generalization
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Libraries
Use these libraries to find Domain Generalization models and implementationsDatasets
Fashion-MNIST
Office-Home
DomainNet
PACS
ImageNet-C
ImageNet-R
ImageNet-A
ImageNet-Sketch
Stylized ImageNet
WildDash
Latest papers
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
We address the online combinatorial choice of weight multipliers for multi-objective optimization of many loss terms parameterized by neural works via a probabilistic graphical model (PGM) for the joint model parameter and multiplier evolution process, with a hypervolume based likelihood promoting multi-objective descent.
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.
SETA: Semantic-Aware Token Augmentation for Domain Generalization
In this paper, we study the impact of prior CNN-based augmentation methods on token-based models, revealing their performance is suboptimal due to the lack of incentivizing the model to learn holistic shape information.
Neural Markov Random Field for Stereo Matching
aeolusguan/NMRF
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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
The existing SDG approaches are mainly for image classification and segmentation, and can hardly be extended to our case due to its regression nature and label ambiguity (i. e., ambiguous pixel-level ground truths).
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain Generalization
We evaluate our method on the complex task of multicenter histopathology image classification, comparing it against state-of-the-art (SOTA) SDG baselines.
A Causal Inspired Early-Branching Structure for Domain Generalization
By d-separation, we observe that the causal feature can be further characterized by being independent of the domain conditioned on the object, and we propose the following two strategies as complements for the basic framework.
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
szc12153/sparse_meta_tuning
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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.
