Fashion-MNIST replaces MNIST for machine learning benchmarks.
problem No new problem introduced.
method No new method introduced.
result Fashion-MNIST serves as a direct replacement for MNIST.
On-device research index
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.
169,181 papers · 148 categories
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48 results for “image replacement”
Reduces memory costs for developing countries by replacing images with text.
problem High memory costs associated with multimodal webpages in developing countries.
method Canonical Correlation Analysis (CCA) to replace high-cost modality (images) with low-cost modality (text).
result Reduces memory costs by at least 83.35% through eye-tracking experiments.
A method to improve few-shot learning using continual local replacement and pseudo labeling.
problem Learning novel classes with limited data.
method Sophisticated network architecture for feature representation and continual local replacement strategy.
result Significantly improved generalization and better decision boundary for classification.
Unsupervised algorithm parses CSG images into CFG without pretraining.
problem Sparse reward problem in unsupervised program synthesis for images.
method Grammar-encoded tree LSTM, entropy regularization, sampling without replacement.
result Recover meaningful programs in large search spaces (up to ).
Let be a closed oriented surface with a marked point, let be a fixed group, and let be a representation such that the orbit of under the action of the mapping class group is finite. We prove that the image of is finite. A similar result holds …
GANs simulate realistic galaxy images.
problem Simulate complex astronomical images efficiently.
method Progressive GANs with Wasserstein cost function.
result Generates naturalistic galaxy images.
SRS improves deep learning accuracy on small image datasets.
problem Training deep neural networks with limited images per class.
method Fixed sequence index sampling with batch replacement.
result Significantly improved classification accuracy on CIFAR-100.
Hybrid bootstrap improves model performance over dropout.
problem Improving predictive model performance through regularization.
method Resamples features from other training points instead of replacing them with zeros.
result Offers superior performance compared to dropout.
DepthNets learns 3D face geometry and transformations without supervision.
problem Learning 3D face geometry and transformations from a single image.
method Unsupervised learning of facial keypoints depth, using backpropable loss for 3D transformations.
result DepthNets can predict 3D transformations and re-target faces to new poses or geometries.
New method improves diversity in GAN-generated images.
problem Lack of diversity in GAN-generated images.
method Introducing moment reconstruction losses to replace the reconstruction loss in GAN training.
result Improved diversity in generated images without sacrificing visual fidelity.
Paper improves dictionary learning by addressing local and global coherence issues.
problem Improving dictionary learning by addressing local and global coherence issues.
method The paper uses the ITKrM algorithm to prove contraction under relaxed conditions and proposes replacing bad dictionaries with carefully designed candidates.
result The adaptive version of ITKrM can recover a generating dictionary from randomly initialized dictionaries of various sizes and learn meaningful dictionaries on image data.
Unified theory for adaptive image convolutions using metric perspectives.
problem Fixed kernels in convolutions limit adaptability in image processing.
method Metric perspective on images as 2D manifolds with local distances, proposing metric convolutions.
result Metric convolutions provide better generalisation and competitive performance.
Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GANeess.IV
CollaGAN learns MR contrast imputation, revealing essential contrasts.
problem Understanding which MR contrast is essential for imputation.
method Collaborative Generative Adversarial Networks (CollaGAN).
result Exogenous contrast from contrast agents is not replaceable, but other endogenous contrasts can be synthesized.
Bayesian layer improves image segmentation and out-of-distribution detection.
problem Outlier detection in image segmentation.
method Parameter-efficient hierarchical convolutional Gaussian Processes in Wasserstein-2 space.
result Uncertainty estimates improve out-of-distribution detection.
MaxDropout removes most active neurons to prevent overfitting in deep neural networks.
problem Preventing overfitting in deep neural networks.
method MaxDropout removes the most active neurons in each hidden layer to enforce sparsity and prevent overfitting.
result MaxDropout improves neural network performance in image classification and achieves comparable results to existing regularizers.
Predicting MRI coil failures using time series classification.
problem Early detection of MRI hardware failures to prevent downtime.
method Training a statistical model on sequential image data features over time.
result LSTMs achieved an F-score of 86.43% and 98.33% accuracy in predicting coil damage.
Localized curvature bounds ensure harmonic maps are constant.
problem Ensuring harmonic maps are constant under localized curvature constraints.
method Localized Bochner-type rigidity theorem for harmonic maps with image-dependent curvature bounds.
result Harmonic maps are constant if minimal Ricci curvature dominates image-dependent curvature bounds.
New SSIM measure improves adversarial attacks' perceptual quality.
problem Lack of perceptual quality in traditional adversarial attacks.
method Replaced norms with SSIM measure for better quality.
result SSIM-constrained attacks achieve better quality and similar success rate.
Fast object detection in JPEG images without decompression.
problem Efficient object detection in compressed JPEG images.
method Modified SSD with DCT coefficients input processing.
result 2x faster detection with promising performance.
We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in q-space. By estimating the signal on a grid, we can do synthetic diffusion spectrum imaging: reconstructing the ensemble averaged propagator (EAP) by an inverse Fourier transform. We also propose an a…
SimLoss improves classification by considering class similarities.
problem Equal punishment of all misclassifications in CCE.
method Integrates class similarities into Categorical Cross Entropy using matrices constructed from task-specific knowledge.
result Significant improvements over CCE on Age Estimation and Image Classification.
Perturbative GAN reduces training complexity and improves image quality.
problem Training complexity and image quality in GANs.
method Replaces convolution layers with perturbation layers that add fixed noise masks.
result Higher inception score and faster convergence of generated images.
Recently, fully-connected and convolutional neural networks have been trained to achieve state-of-the-art performance on a wide variety of tasks such as speech recognition, image classification, natural language processing, and bioinformatics. For classification tasks, most of these "deep learning" models employ the so…
Adaptive convolution improves GANs performance on image generation.
problem GANs struggle with generating images of objects with diverse appearances.
method Proposes adaptive convolution to learn upsampling based on local context.
result Adaptive convolution models improve GANs performance on CIFAR-10 and STL-10 datasets.
Develops counterfactual visual explanations to show how images could change to classify differently.
problem Creating understandable explanations for vision system predictions.
method Selects a distractor image and identifies spatial regions to modify for different classification.
result Users trained with counterfactual explanations perform better in fine-grained bird classification.
Learning based methods have shown very promising results for the task of depth estimation in single images. However, most existing approaches treat depth prediction as a supervised regression problem and as a result, require vast quantities of corresponding ground truth depth data for training. Just recording quality d…
Improved assessment of knee osteoarthritis using geodesic B-score.
problem Need for automatic, reader-independent measures of osteoarthritis clinical outcomes.
method Derive a geodesic B-score for Riemannian shape spaces, develop efficient algorithm for large shape populations.
result Geodesic B-score exhibits improved discrimination ability over Euclidean B-score.
Enhanced Neural ODEs outperform traditional models in image classification and video prediction.
problem Efficiently modeling time-varying dynamics in neural networks.
method Proposed a novel family of non-autonomous Neural ODEs with time-varying weights.
result Outperformed previous Neural ODE variants in speed and representational capacity.
Medical image reconstruction advances from sparse models to machine learning.
problem Improving image quality and reducing noise in medical imaging.
method Iterative reconstruction, modified data acquisition methods, and machine learning models.
result Machine learning methods show promise in improving image quality.
RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse priorcs.LG
RG-Flow combines RG and sparse priors for hierarchical image disentanglement.
problem Disentangling and manipulating image representations at different scales.
method Hierarchical flow model using RG and sparse prior distributions.
result RG-Flow enables semantic manipulation and style mixing at different image scales.
Efficient deep learning for CT images reduces memory and training time.
problem Training deep neural networks for CT images is computationally expensive.
method Unrolled proximal gradient descent, replaced penalty terms with CNNs, used greedy learning with deep UNet and surrogate.
result Achieved comparable image quality to state-of-the-art methods on CT image reconstruction challenges.
PoGDiff improves diffusion models on imbalanced datasets.
problem Diffusion models struggle with imbalanced text-image pairs.
method PoGDiff replaces ground-truth with a Product of Gaussians.
result PoGDiff enhances generation accuracy and quality on imbalanced datasets.
Improved image classification using centroids and stochastic sampling.
problem Limited accuracy of nearest-neighbor classification.
method Coarse-graining (replacing images by centroids) and stochastic sampling of centroids.
result Stochastic sampling of centroids improves classification accuracy.
Advances in deep learning for natural images have prompted a surge of interest in applying similar techniques to medical images. The majority of the initial attempts focused on replacing the input of a deep convolutional neural network with a medical image, which does not take into consideration the fundamental differe…
BayPrAnoMeta tackles few-shot industrial image anomaly detection with Bayesian methods.
problem Challenges in industrial image anomaly detection, especially class imbalance and scarcity of labeled samples.
method Bayesian Proto-MAML approach with probabilistic normality models and Bayesian posterior predictive likelihood.
result Consistent and significant AUROC improvements over existing methods in few-shot anomaly detection.
This work proposes a novel privacy-preserving method for synthetic replacement of sensitive data.
problem Privacy-preserving transformations of sensitive data.
method Adversarial representation learning for synthetic replacement of private attributes.
result Our method provides stronger privacy and better utility than previous methods.
Rényi Neural Processes replace KL divergence with Rényi divergence to improve NP performance.
problem Parameterization coupling in Neural Processes leads to prior misspecification.
method Propose Rényi Neural Processes (RNP) by replacing KL divergence with Rényi divergence.
result Significant performance improvements in real-world problems, including better log-likelihoods.
CAggNet improves medical image segmentation by fusing coarse and fine features.
problem Medical image segmentation accuracy and efficiency.
method Crossing Aggregation Network with nested skip connections and weighted aggregation.
result CAggNet achieves more accurate and efficient segmentation compared to existing methods.
SAN replaces global pooling layers, improving accuracy and reducing overfitting.
problem Improving accuracy and reducing overfitting in deep neural networks.
method Introducing Set Aggregation Network (SAN) as a trainable global pooling layer.
result SAN improves classification accuracy and is less prone to overfitting.
Proposes Moment Exchange to use moments in image recognition models, improving generalization.
problem Discarding moments in image recognition models reduces stability and training time.
method Moment Exchange: replaces moments of learned features with another image's moments and interpolates labels.
result Improves generalization of recognition models across multiple datasets.
Harmonic maps intersect all minimal surfaces with bounded curvature.
problem Intersection of harmonic maps with minimal surfaces.
method Nonconstant conformal harmonic maps intersecting bounded curvature minimal surfaces.
result Harmonic maps intersect every nonflat properly embedded minimal surface of bounded curvature.
Counting and classifying blood cells is an important diagnostic tool in medicine. Support Vector Machines are increasingly popular and efficient and could replace artificial neural network systems. Here a method to classify blood cells is proposed using SVM. A set of statistics on images are implemented in C++. The MPE…
The augmented Lagrangian (AL) method that solves convex optimization problems with linear constraints has drawn more attention recently in imaging applications due to its decomposable structure for composite cost functions and empirical fast convergence rate under weak conditions. However, for problems such as X-ray co…
Quadratic autoencoder improves low-dose CT image denoising.
problem Low-dose CT image denoising.
method Quadratic autoencoder architecture applied to CT denoising.
result Quadratic autoencoder achieves superior denoising performance and efficiency.
Involutive distributions of operator-valued evolutionary vector fields and their affine geometry. IImath-ph
We generalize the notion of a Lie algebroid over infinite jet bundle by replacing the variational anchor with an N-tuple of differential operators whose images in the Lie algebra of evolutionary vector fields of the jet space are subject to collective commutation closure. The linear space of such operators becomes an a…
New priors improve Bayesian neural networks without cooling.
problem Bayesian neural networks underfit on clean datasets.
method Introduce DirClip and confidence priors to replace cooling.
result DirClip and confidence priors outperform cold posterior.
Automated system identifies and counts insects from images.
problem Manual sorting and identification of insect samples is time-consuming and limits biodiversity mapping.
method Robot-enabled image-based identification machine using CNNs.
result Classification accuracy of for initial dataset.
Cascade adversarial training improves robustness against iterative attacks.
problem Improving robustness against unknown iterative attacks.
method Cascade adversarial training, incorporating adversarial images and embedding space for low-level similarity learning.
result Enhanced robustness against iterative attacks, but decreased robustness against one-step attacks.