CoDAG combines domain adaptation and generalization for unsupervised continual domain shift learning.
problem Acquiring knowledge in unsupervised continual domain shift learning.
method Complementary Domain Adaptation and Generalization (CoDAG) framework.
result CoDAG outperforms state-of-the-art models in all datasets and evaluation metrics.
Method learns domain-specific representations without supervision.
problem Domain generalization without labeled data.
method Hierarchical variational autoencoder approach.
result Model generalizes to unseen domains without domain supervision.
CSD learns a common component for domain generalization, outperforming existing methods.
problem Training models to generalize across unseen domains.
method CSD decomposes the model into a common and specific component, discarding the latter.
result CSD outperforms state-of-the-art domain generalization methods.
DCASE 2022 Task 2 tackles domain shifts in ASD for machine condition monitoring.
problem Domain shifts change acoustic characteristics, affecting ASD performance.
method Domain generalization techniques to detect anomalies across unknown domains.
result Two types of domain generalization techniques were identified and analyzed.
We address the problem of domain generalization where a decision function is learned from the data of several related domains, and the goal is to apply it on an unseen domain successfully. It is assumed that there is plenty of labeled data available in source domains (also called as training domain), but no labeled dat…
TCRI improves domain generalization by enforcing conditional independence constraints.
problem Limitations of existing domain generalization methods due to incomplete constraints.
method TCRI implements regularizers motivated by conditional independence constraints.
result TCRI achieves cross-domain stability and outperforms baselines in worst-domain accuracy.
Proposes DFDG for robust domain generalization without source domain labels.
problem Robustness of deep learning models in real-world applications where train and test distributions differ.
method Model-agnostic, class-aware alignment of class relationships through saliency maps.
result Competitive performance on time series sensor and image classification datasets.
Study shows how many domains are needed for generalization, using a new measure called domain shattering dimension.
problem How many domains are needed for domain generalization?
method Introduced a new combinatorial measure called the domain shattering dimension to model domain sample complexity.
result Established a tight quantitative relationship between domain shattering dimension and classic VC dimension.
TAROT enhances robustness and domain adaptability with domain-invariant features.
problem Developing models robust to adversarial attacks across diverse domains.
method Derives a new generalization bound and proposes TAROT algorithm.
result TAROT outperforms state-of-the-art methods in accuracy and robustness.
Method generates intermediate domains to align source and target domains.
problem Challenges of domain adaptation with significant domain divergence.
method Progressive domain augmentation via domain interpolation and multiple subspace alignment.
result Achieves state-of-the-art performance on multiple domain adaptation tasks.
Generates counterfactuals in target domain from source domain observations.
problem Cross-domain learning with domain shifts and lack of parallel datasets.
method Unsupervised, Neural Causal Models, Joint Causal Graphs, Effect-Intrinsic vs Domain-Intrinsic Variables.
result Framework generates counterfactuals that closely match ground truth.
This paper addresses classification tasks on a particular target domain in which labeled training data are only available from source domains different from (but related to) the target. Two closely related frameworks, domain adaptation and domain generalization, are concerned with such tasks, where the only difference …
The paper examines domain generalization algorithms and finds empirical risk minimization performs well.
problem Comparing domain generalization algorithms is difficult due to inconsistent experimental conditions.
method Implemented DomainBed, a testbed for domain generalization with seven datasets and model selection criteria.
result Empirical risk minimization shows state-of-the-art performance across all datasets.
We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain. Most existing domain adaptation methods attempt to erase domain signals using technique…
A sequential learning framework improves domain generalization performance.
problem Training models robust to domain shift across multiple domains.
method Inspired by lifelong learning, a sequential training approach optimizing for all following domains.
result Improves performance on DG benchmarks with a simple, fast algorithm.
An essential problem in domain adaptation is to understand and make use of distribution changes across domains. For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains. By explicitly…
Paper proposes CCVAE for generalized zero-shot domain adaptation.
problem Adapting to unseen classes in target domain with limited labeled data.
method Coupled Conditional Variational Autoencoder (CCVAE).
result CCVAE generates synthetic target domain features for unseen classes.
New framework tackles DG under posterior drift, where optimal classifier varies by domain.
problem Generalizing from multiple domains with varying optimal classifiers.
method Decision-theoretic framework for DG under posterior drift.
result Optimal classifier can vary significantly across domains, challenging existing DG approaches.
A method identifies domain-general features using causal graph constraints and regularization.
problem Identifying domain-general features without prior knowledge of spurious features.
method Proposes a novel regularization framework based on causal graph constraints.
result Demonstrates effectiveness in both synthetic and real-world data, outperforming state-of-the-art methods.
DGSAM improves domain generalization by minimizing individual sharpness.
problem Improving domain generalization models that perform well on unseen target domains.
method Shifts DG paradigm toward minimizing individual sharpness across source domains.
result DGSAM reduces performance variance across domains with less computational overhead.
A new method for domain generalization using unlabeled data.
problem Learning from multiple domains with limited labeled data.
method Combines meta learning and semi-supervised learning with entropy-based pseudo-labeling and discrepancy loss.
result Significantly outperforms state-of-the-art methods on benchmark datasets.
Paper develops upper-bounds for target general loss in multiple source DA and DG settings.
problem Complexity and trade-offs in multiple source domain adaptation and domain generalization.
method Defines two types of domain-invariant representations and studies their pros, cons, and trade-offs.
result Developed upper-bounds for target general loss offer insights into domain-invariant representations.
New method improves domain generalization by aligning causal mechanisms across domains.
problem Improving model's ability to generalize across different distributions.
method Introduces invariance of average causal effect of features to labels, regularizing training approach.
result Demonstrates superior performance on benchmark datasets compared to state-of-the-art methods.
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to individual domains. Our approach employs multiple normalization methods while learning separate affine parameters per domain. For each domain,…
New meta-learning method improves domain generalization by balancing parameters closer to domain centroids.
problem Improving domain generalization by reducing overfitting to specific domains.
method Arithmetic meta-learning with arithmetic-weighted gradients to balance parameters closer to domain centroids.
result Experimental validation of improved domain generalization performance.
Paper introduces Influence Function to assess OOD generalization stability.
problem Assessing OOD generalization accuracy when target domains are unknown.
method Introduced Influence Function from robust statistics to monitor model stability.
result Accuracy on test domains and Influence Function variance can distinguish OOD algorithms and generalization quality.
COLUMBUS discovers new features to improve domain generalization.
problem Improving machine learning models' ability to generalize to unseen domains.
method COLUMBUS uses targeted corruption of input and multi-level representations to discover new features.
result COLUMBUS achieves state-of-the-art performance on DG benchmarks.
Gradual domain adaptation improves model transfer between domains with intermediate training.
problem Challenges in unsupervised domain adaptation when distribution shifts are large.
method Gradual self-training using intermediate domains along the Wasserstein geodesic.
result GOAT framework generates intermediate domains for improved adaptation.
Proves a generalized Minkowski inequality for starshaped domains.
problem Proving a generalized Minkowski inequality for smooth, (k−1)-convex starshaped domains. method Solvability of the degenerate k-Hessian equation on the exterior domain Rn∖Ω. result Generalized Minkowski inequality holds for smooth, (k−1)-convex, starshaped domains. ADGAN improves risk tolerance prediction by aligning cross-domain data.
problem Lack of professional knowledge and domain-specific models in risk tolerance studies.
method Asymmetric cross-Domain Generative Adversarial Network (ADGAN) for domain scale inequality.
result ADGAN better handles class imbalance and unqualified data than state-of-the-art methods.
Deep learning struggles with out-of-distribution data, so this paper tackles domain generalization.
problem Deep learning models fail with out-of-distribution data.
method Formulates domain generalization as a constrained statistical learning problem, then uses nonconvex duality theory to develop an algorithm with convergence guarantees.
result Improves domain generalization by up to 30 percentage points on various benchmarks.
Generalized score matching for densities on general domains.
problem Estimating density functions on complex, non-standard domains.
method A generalized score matching approach for densities on general domains.
result The method provides theoretical guarantees and empirical advantages.
This paper tackles continuous domain generalization, improving model performance across unseen domains.
problem Existing domain generalization approaches fail to capture the complex, multidimensional nature of real-world variation.
method Introduces Continuous Domain Generalization (CDG), a principled framework grounded in geometric and algebraic theories. Proposes a Neural Lie Transport Operator (NeuralLio) for structure-preserving parameter transitions and a gating mechanism for robust generalization.
result Demonstrates significant improvement in generalization accuracy and robustness across various datasets.
In this work, we present an interesting attempt on mixture generation: absorbing different image concepts (e.g., content and style) from different domains and thus generating a new domain with learned concepts. In particular, we propose a mixture generative adversarial network (MIXGAN). MIXGAN learns concepts of conten…
Domain Generation Algorithms (DGAs) are frequently used to generate numerous domains for use by botnets. These domains are often utilized as rendezvous points for servers that malware has command and control over. There are many algorithms that are used to generate domains, however many of these algorithms are simplist…
This paper investigates domain generalization: How to take knowledge acquired from an arbitrary number of related domains and apply it to previously unseen domains? We propose Domain-Invariant Component Analysis (DICA), a kernel-based optimization algorithm that learns an invariant transformation by minimizing the diss…
New PCGML approach generates novel game content across multiple platformer domains.
problem Generating novel game content in new domains.
method Using a new affordance and path vocabulary, variational autoencoders trained on data from six platformer games produce new content with varying proportions of different domains.
result Captures latent level space spanning multiple domains and generates new content with varying proportions of different domains.
Domain adaptation algorithms are designed to minimize the misclassification risk of a discriminative model for a target domain with little training data by adapting a model from a source domain with a large amount of training data. Standard approaches measure the adaptation discrepancy based on distance measures betwee…
New method improves domain adaptation by aligning sub-domains.
problem Real-world domain adaptation with shifted class distributions.
method Rebalanced sub-domain alignment and novel generalization bound.
result Improves performance in shifted class distribution scenarios.
Conditional domain generation is a good way to interactively control sample generation process of deep generative models. However, once a conditional generative model has been created, it is often expensive to allow it to adapt to new conditional controls, especially the network structure is relatively deep. We propose…
Adversarial techniques learn invariant representations across multiple domains.
problem Domain generalization from diverse studies to unseen domains.
method Adversarial censoring techniques for invariant representation learning.
result Limiting behavior of adversarial loss function as the number of domains grows.
Paper tackles domain generalization by minimizing domain-based covariance.
problem Training data and test data have different distributions, leading to poor generalization.
method Find a central subspace minimizing domain-based covariance while preserving functional relationships.
result The proposed method achieves better generalization performance on unseen test datasets.
DRDA robustly adapts models across domains with mismatched distributions.
problem Vulnerability of DA methods to noise and inability to generalize to unseen samples.
method DRDA uses distributionally robust optimization (DRO) with MMD metric to learn robust decision functions.
result DRDA outperforms existing robust learning approaches in experiments.
Causal predictors don't generalize better across domains than non-causal predictors.
problem How well do causal predictors generalize across different domains?
method 16 prediction tasks on tabular datasets, selecting causal features.
result Causal predictors do not outperform non-causal predictors in domain generalization.
We define self-adjoint extensions of the Hodge Laplacian on Lipschitz domains in Riemannian manifolds, corresponding to either the absolute or the relative boundary condition, and examine regularity properties of these operators' domains and form domains. We obtain results valid for general Lipschitz domains, and stron…
Unsupervised domain adaptation studies the problem of utilizing a relevant source domain with abundant labels to build predictive modeling for an unannotated target domain. Recent work observe that the popular adversarial approach of learning domain-invariant features is insufficient to achieve desirable target domain …
Domain generalization is the problem of machine learning when the training data and the test data come from different data domains. We present a simple theoretical model of learning to generalize across domains in which there is a meta-distribution over data distributions, and those data distributions may even have dif…
Study reveals adversarially robust domain adaptation is harder to generalize across domains.
problem Hardness of transferring adversarial robustness across different domains.
method Analysis of adversarial Rademacher complexity over symmetric difference hypothesis space.
result Adversarial Rademacher complexity is always greater than non-adversarial, indicating intrinsic hardness.