Dataset for object detection at Oktoberfest beer tent.
problem Realistic object detection in a busy, diverse setting.
method Hand-annotated 1,110 images, provided trained models.
result Challenging dataset for object detection.
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arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
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48 results for “Realistic dataset”
Open dataset and pipeline for realistic OPE research.
problem Lack of realistic and reproducible OPE experimental studies.
method Public logged bandit dataset and Python software.
result Enables experimental comparisons of OPE estimators.
SKADA-bench evaluates unsupervised DA methods across diverse modalities.
problem Evaluating unsupervised DA methods on diverse modalities with realistic validation.
method Nested cross-validation and unsupervised model selection scores.
result Highlights the importance of realistic validation and provides practical guidance.
Proposes a unified normalization method for multi-domain medical images.
problem Inadequate joint information across multiple datasets hinders image segmentation performance.
method Adversarial and task-driven normalization approach to learn a common normalizing function across multiple datasets.
result Jointly normalized images improve segmentation accuracy by up to 57.5%.
VQ-DRAW compresses images and generates realistic samples.
problem Learning compact discrete representations of images.
method Sequential discrete VAE with vector quantization.
result VQ-DRAW effectively compresses and generates images.
New method to measure compositional generalization on realistic data.
problem Limited compositional generalization in machine learning.
method Maximizing compound divergence while ensuring small atom divergence.
result Machine learning architectures fail to generalize compositionally.
GANs generate realistic financial data for research.
problem Creating realistic financial datasets for research and benchmarking.
method Proposed a novel type of GAN and methods for preprocessing and evaluation.
result GANs can replicate financial datasets with high fidelity.
Generates realistic time-series data from causal models.
problem Simulate realistic time-series data from causal models.
method Adversarial Causal Tuning (ACT) methodology.
result ACT selects optimal causal models and quantifies goodness-of-fit.
Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to produce realistic real-valued multi-dimensional time series, with an emphasis on their appli…
Algorithm generates realistic metaorders from public trade data.
problem Generating realistic metaorders from public data.
method Novel algorithm that recovers stylized facts of metaorders impact.
result Average realized short-term price impact has a mechanical origin.
CausalTime generates realistic time-series for TSCD evaluation.
problem Lack of realistic synthetic datasets for TSCD performance evaluation.
method Harnessing deep neural networks and normalizing flow for dynamics, extracting causal graphs, and deriving ground truth causal graphs.
result Generated datasets accurately reflect real data and ground truth causal graphs.
Proposes a method to generate realistic counterfactuals by learning relationships.
problem Counterfactual explanations often ignore intrinsic relationships between data attributes.
method Uses a variational auto-encoder to learn relationships and perturb the latent space.
result The model preserves relationships and generates realistic counterfactuals.
OGB provides diverse graph datasets for robust ML research.
problem Challenges in scalable and robust graph machine learning.
method Unified evaluation protocol, diverse datasets, and automated pipeline.
result Significant scalability and generalization challenges identified.
In this paper we investigate the feasibility of using synthetic data to augment face datasets. In particular, we propose a novel generative adversarial network (GAN) that can disentangle identity-related attributes from non-identity-related attributes. This is done by training an embedding network that maps discrete id…
Unsupervised learning representations generalize better than supervised learning under distribution shifts.
problem Robustness of unsupervised representations to distribution shift.
method Extensive evaluation on synthetic and realistic datasets, including controllable domain generalization datasets.
result Unsupervised representations learned from SSL and AE generalize better than supervised learning under various distribution shifts.
Paper generalizes teacher-student model for realistic data.
problem Capturing learning curves for realistic datasets.
method Introduces a Gaussian covariate generalization of the teacher-student model.
result Generalized model captures learning curves for various realistic data sets.
SpaCE tackles spatial confounding in scientific studies.
problem Spatial confounding influences treatment and outcome, leading to spurious associations.
method Introduces SpaCE toolkit for benchmark datasets and tools to evaluate causal inference methods.
result Facilitates automated evaluation of machine learning and causal inference models.
Early detection of breast cancer has a major contribution to curability, and using mammographic images, this can be achieved non-invasively. Supervised deep learning, the dominant CADe tool currently, has played a great role in object detection in computer vision, but it suffers from a limiting property: the need of a …
Bayesian model selection improves causal discovery in complex datasets.
problem Identifying causal direction in Markov equivalence classes with realistic assumptions.
method Incorporating causal assumptions within Bayesian framework for model selection.
result Bayesian model selection outperforms previous methods on various datasets.
This work uses GANs to generate realistic vehicle test inputs.
problem Lack of realistic input sequences for automotive testing.
method Applying GANs to learn from unlabeled in-vehicle signals and generate synthetic inputs.
result Improved virtual test coverage and reduced need for expensive field tests.
AI-GAN generates realistic adversarial examples efficiently.
problem Efficient generation of perceptually realistic adversarial examples.
method A joint training of a generator, discriminator, and attacker.
result Achieves high attack success rates and reduces generation time.
Evaluates transfer learning methods in dynamic data availability scenarios.
problem Real-world data availability varies over time, leading to unrealistic TL method evaluations.
method Proposes a data manipulation framework to simulate varying data availability and domain transformations.
result Demonstrates the usefulness of the framework on proprietary and publicly available datasets.
Federated learning studies separate client data and distribution gaps.
problem Understanding performance differences in federated learning across different datasets.
method Proposed a framework to disentangle out-of-sample and participation gaps.
result Dataset synthesis strategy is crucial for realistic simulations of federated learning generalization.
Study evaluates scalability and real-world impact of disentangled representations.
problem Scalability and real-world impact of disentangled representations.
method New high-resolution dataset and architectures for disentangled representation learning.
result Disentanglement predicts out-of-distribution task performance.
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.
Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In…
GANs trained on artificial datasets avoid dataset biases, revealing generative model weaknesses.
problem GANs trained on real datasets often underfit or overfit, making analysis difficult.
method Trained GANs on artificial datasets with infinite samples and simple distributions.
result GANs fail to learn optimal parameters, suggesting limitations in generative models.
New bucketing scheme improves Byzantine robustness for heterogeneous data.
problem Byzantine attacks on federated learning with heterogeneous data.
method Bucketing scheme to adapt robust algorithms to non-iid data.
result Bucketing scheme ensures convergence against Byzantine attacks.
Develops workflow for synthetic insurance datasets.
problem Lack of realistic publicly available insurance datasets.
method Uses CTGAN neural network architecture to generate tabular data.
result Synthesized datasets evaluated positively in multiple aspects.
Adapting deep generative approaches for getting synthetic data with realistic marginal distributionsstat.ML
A new method generates synthetic data with realistic marginal distributions.
problem Generating synthetic data with bimodal and skewed marginal distributions.
method Pre-transformation variational autoencoders (PTVAEs) with separate parameter optimization for each variable.
result PTVAEs outperform other methods in generating synthetic data with bimodal and skewed distributions.
Paper generates synthetic financial transactions for AML model testing.
problem Insufficient real data for AML model training.
method Agent-based generator calibrated to real transactions.
result Synthetic datasets improve model comparison by providing complete ground truth.
This paper presents a novel CNN-based approach for synthesizing high-resolution LiDAR point cloud data. Our approach generates semantically and perceptually realistic results with guidance from specialized loss-functions. First, we utilize a modified per-point loss that addresses missing LiDAR point measurements. Secon…
We extend and improve the work of Model Agnostic Anchors for explanations on image classification through the use of generative adversarial networks (GANs). Using GANs, we generate samples from a more realistic perturbation distribution, by optimizing under a lower dimensional latent space. This increases the trust in …
Proposes a framework to assess model robustness to dataset shifts.
problem Evaluating model robustness to changes in setting or population.
method Derives a debiased estimator for analyzing performance on worst-case distributions.
result Demonstrates the estimator can account for realistic shifts in complex distributions.
InvGAN combines generative and inference models for photo-realistic image manipulation.
problem GANs lack an inference model for image editing and downstream tasks.
method Train inference and generative models together to adapt and converge.
result InvGAN embeds real images into a high-quality generative model's latent space.
Enhances anomaly detection using multiple reference datasets.
problem Lack of performance with multiple reference datasets.
method Generalizes existing techniques CWoLa and SALAD for multiple reference datasets.
result Improved performance across various settings.
This work evaluates PDA methods without target labels, revealing significant accuracy drops.
problem Evaluating PDA methods without target labels and inconsistent experimental settings.
method Realistic evaluation of 7 PDA methods with 7 model selection strategies on 2 datasets.
result Accuracy drops up to 30 percentage points without target labels, only one method performs well.
Framework generates multimodal datasets with known MI for benchmarking.
problem Benchmarking mutual information estimators and SSL techniques.
method Flow-based generative model and structured causal framework.
result Regression performance improves with increasing MI between modalities.
Develops a new model for controllable and realistic traffic simulation.
problem Lack of models that offer both controllability and realism in traffic simulation.
method Guided Conditional Diffusion (CTG) model using diffusion modeling and differentiable logic.
result Improves controllability-realism tradeoff over strong baselines.
Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle it, we find the procedure and datasets that are used to assess their progress lacking. To address this limitation, we propose Meta-Dataset: a new benchmark for training an…
SCARY dataset generates complex causal scenarios for causality research.
problem Lack of complexity in existing causal datasets.
method Synthetic dataset with 40 scenarios, three seeds, and two data generation mechanisms.
result Provides a valuable resource for realistic causal discovery.
CounteRGAN generates realistic, actionable counterfactuals for machine learning models.
problem Creating realistic and actionable counterfactuals for machine learning models.
method Applying Residual GANs to improve counterfactual realism and actionability.
result CounteRGAN produces counterfactuals with improved realism and actionability, achieving real-time applicability.
A new method for generating counterfactual explanations in high-dimensional datasets.
problem Creating realistic counterfactual explanations in complex, high-dimensional data.
method A discretized approach using binary search and boundary approximation.
result Our method reduces the distance between counterfactuals by 5% to 50% in terms of the L2 norm.
SBSS uses similarity to split data for better classifier training.
problem Training better classifiers with realistic performance estimation.
method SBSS uses both input and output space information to split data using similarity functions.
result SBSS outperformed ordinary stratified 10-fold cross-validation in 75% of scenarios.
CNN improves AMC accuracy under multipath fading channels.
problem Difficulty in designing robust AMC classifiers under multipath fading.
method Proposes a CNN classifier for AMC, robust to channel impairments.
result CNN outperforms existing methods in accuracy and training time.
Robust OPE framework uses human inputs to improve policy evaluation in changing environments.
problem Inaccurate policy evaluations due to shifts in environment properties.
method Adapts OPE methods to shifts on user-inputted covariates, providing more realistic utility estimates.
result Robust OPE framework yields less pessimistic policy evaluations and captures realistic dataset shifts.
Generative Adversarial Networks create realistic brain MRI images.
problem Lack of sufficient training data for medical research.
method Experimented three GAN architectures with different loss functions and hyperparameter tuning.
result Achieved high-quality, realistic synthetic MRIs with gradient penalty and mini-batch similarity layer.
Transformers learn to recall with non-orthogonal embeddings in realistic settings.
problem Understanding how transformers store and retrieve knowledge in practical scenarios.
method Analyzing a single-layer transformer with random embeddings trained on a token-retrieval task.
result Explicit formulas for the model's storage capacity reveal a multiplicative dependence on sample size, embedding dimension, and sequence length.