Memory-efficient learning for large-scale imaging systems.
problem Memory limitations in GPUs for real-world large-scale inverse problems.
method Exploits reversibility of network layers to enable data-driven design.
result Demonstrated on small-scale and large-scale real-world systems.
Content based image retrieval, a technique which uses visual contents of image to search images from large scale image databases according to users' interests. This paper provides a comprehensive survey on recent technology used in the area of content based face image retrieval. Nowadays digital devices and photo shari…
Hierarchical autoregressive models improve image quality by learning abstract representations.
problem Local structure bias in autoregressive models leads to lack of large-scale coherence in generated images.
method Propose two methods to learn discrete representations of images that abstract away local detail, and train autoregressive priors on these representations.
result Hierarchical autoregressive models produce high-fidelity reconstructions and realistic images with large-scale coherence.
New DAM method improves AUC scores in medical image classification.
problem Maximizing AUC in large-scale medical image classification.
method Proposes AUC margin loss for robust optimization, conducts extensive empirical studies.
result Improves performance on four medical image classification tasks, achieving 1st place on Stanford CheXpert.
This paper considers the problem of inferring image labels from images when only a few annotated examples are available at training time. This setup is often referred to as low-shot learning, where a standard approach is to re-train the last few layers of a convolutional neural network learned on separate classes for w…
HET-XL improves heteroscedastic classifiers for large-scale image classification.
problem Scaling heteroscedastic classifiers to handle large numbers of classes and tuning the temperature hyperparameter.
method HET-XL, a heteroscedastic classifier with independent parameter count from the number of classes, learns the temperature hyperparameter directly from training data.
result HET-XL requires 14X fewer additional parameters and performs better than baseline heteroscedastic classifiers on large image classification datasets.
Natural image statistics exhibit hierarchical dependencies across multiple scales. Representing such prior knowledge in non-factorial latent tree models can boost performance of image denoising, inpainting, deconvolution or reconstruction substantially, beyond standard factorial "sparse" methodology. We derive a large …
Generates coherent storybooks from plain text using diffusion models.
problem Ensuring coherency in a sequence of images for storytelling applications.
method Combines pre-trained LLM and text-guided Latent Diffusion Model for zero-shot generation.
result Outperforms state-of-the-art image editing baselines in generating coherent storybooks.
This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space …
A distributed algorithm learns patterns in large images and signals.
problem High-dimensional optimization in large images and signals.
method Distributed asynchronous algorithm with locally greedy coordinate descent.
result Patterns can be learned on large scales images from the Hubble Space Telescope.
This research shows unsupervised GANs can perform object segmentation without labels.
problem Performing object segmentation without pixel or image-level labels.
method Used large-scale unsupervised GAN models to differentiate foreground from background.
result Demonstrated high-quality saliency masks and new state-of-the-art performance.
Super-resolution methods form high-resolution images from low-resolution images. In this paper, we develop a new Bayesian nonparametric model for super-resolution. Our method uses a beta-Bernoulli process to learn a set of recurring visual patterns, called dictionary elements, from the data. Because it is nonparametric…
A new neural network approach for medical image reconstruction.
problem Ill-posed large-scale inverse problems in medical imaging.
method Decoupling regularization from data consistency using neural network priors.
result The method outperforms existing techniques in various quantitative measures.
Proposes a method for forecasting large-scale interval-valued time series.
problem Modeling and forecasting large-scale interval-valued time series.
method Feature extraction procedure involving auto-segmentation, clustering, and precision matrix estimation.
result The method enhances forecasting performance for large-scale interval-valued time series.
A new method resolves permutation issues in shuffled linear regression for large-scale applications.
problem Estimating latent features through linear transformation with unknown permutations.
method Spectral matching method to align spectral components of measurement and feature covariances.
result Achieves accurate estimates in shuffled LS and LASSO settings with sufficient samples.
This work uses image generation models to find vision model bugs.
problem Automatically discovering failures in vision models.
method Conditional text-to-image generation and captioning models.
result Demonstrated utility of large-scale generative models to find vision model bugs.
Scalable PnP-ADMM for large-scale imaging problems.
problem Heavy computational and memory requirements of current PnP algorithms.
method Incremental variant of PnP-ADMM with theoretical convergence guarantees.
result Fast convergence and scalability compared to existing PnP algorithms.
PePR scores assess DL model performance per resource unit, promoting smaller, more efficient models.
problem Limited access to large-scale resources hinders medical image analysis research.
method Introduced PePR score to measure DL model performance per resource unit.
result Small-scale, specialized models outperform large-scale models in resource-constrained settings.
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and many methods are cur…
Probabilistic atlases provide essential spatial contextual information for image interpretation, Bayesian modeling, and algorithmic processing. Such atlases are typically constructed by grouping subjects with similar demographic information. Importantly, use of the same scanner minimizes inter-group variability. Howeve…
The artistic style of a painting is a subtle aesthetic judgment used by art historians for grouping and classifying artwork. The recently introduced `neural-style' algorithm substantially succeeds in merging the perceived artistic style of one image or set of images with the perceived content of another. In light of th…
Graph-RISE learns image embeddings for ultra-fine-grained semantics.
problem Learning image representations for fine-grained semantics.
method Graph-regularized neural graph learning framework.
result Graph-RISE outperforms state-of-the-art on image classification and triplet ranking.
Develops algorithms for efficient visual data compression and search.
problem High-dimensional, large-scale visual data representation.
method Discrete synthesis and analysis models, RRQ and ML-STC frameworks.
result Fast query times and shorter database storage for similarity search.
A new large-scale tabular benchmark for Learning from Label Proportions.
problem Lack of a large-scale open benchmark for tabular Learning from Label Proportions.
method Proposed LLP-Bench, a suite of 70 datasets (62 feature bag and 8 random bag) from real-world tabular data.
result Demonstrated the effectiveness of 9 SOTA and popular tabular LLP techniques on 62 feature bag datasets.
Detects and corrects adversarial images using image processing.
problem Vulnerability of deep neural networks to adversarial attacks.
method Image processing operations to detect and correct adversarial images.
result The method effectively detects and corrects adversarial images.
Improved image classification accuracy with a probabilistic model of label noise.
problem Noisy labels in large-scale image classification datasets.
method A probabilistic model using a multivariate Normal distribution on the final hidden layer of a neural network, capturing input-dependent label noise.
result Significantly improved accuracy on various datasets compared to standard methods.
New method trains deep ResNets without normalization, achieving state-of-the-art performance.
problem Training deep ResNets without normalization layers leads to instability and lower accuracy.
method Adaptive gradient clipping and Normalizer-Free ResNets design.
result Normalizer-Free ResNets achieve 86.5% top-1 accuracy on ImageNet, matching EfficientNet-B7.
DeepEthnic classifies faces into ethnic groups with high accuracy.
problem Classifying faces into ethnic groups using machine learning.
method Transfer learning from a large-scale data recognition network.
result State-of-the-art success rates for four ethnic groups.
High-resolution satellite imagery have been increasingly used on remote sensing classification problems. One of the main factors is the availability of this kind of data. Even though, very little effort has been placed on the zebra crossing classification problem. In this letter, crowdsourcing systems are exploited in …
We present a simple but powerful reinterpretation of kernelized locality-sensitive hashing (KLSH), a general and popular method developed in the vision community for performing approximate nearest-neighbor searches in an arbitrary reproducing kernel Hilbert space (RKHS). Our new perspective is based on viewing the step…
MDEQ models learn multi-resolution features efficiently.
problem Large-scale, hierarchical pattern recognition.
method Implicit differentiation, multiscale deep equilibrium model.
result MDEQs achieve performance on par with recent models.
Proposes a method to train deep neural networks robust to label noise in remote sensing images.
problem Training deep neural networks on datasets with inaccurate labels leads to overfitting and poor performance.
method Uses entropic optimal transport to learn robust deep neural networks.
result Empirically demonstrates superior performance compared to state-of-the-art methods on remote sensing datasets.
SDHR improves data hashing for better classification accuracy.
problem Efficient data hashing for high-dimensional data retrieval.
method Supervised Discrete Hashing with Relaxation (SDHR) using optimized regression targets.
result SDHR outperforms traditional methods in classification accuracy.
fastMRI dataset helps machine learning for MRI faster, cheaper.
problem Accelerating MRI to reduce costs and patient stress.
method Open dataset and benchmarks for machine learning.
result Machine learning can reconstruct MRI images from fewer data.
New privacy mechanisms allow fitting large-scale models without degrading utility.
problem Maintaining privacy in large-scale decentralized learning.
method Reconceptualizing local differential privacy protections against limited prior information.
result Practical locally differentially private mechanisms for all privacy levels.
Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited t…
DPEs use ensembles to approximate BNNs for efficient large-scale visual active learning.
problem Efficiently annotating data for deep neural networks training.
method Deep Probabilistic Ensembles (DPEs) using regularized ensemble approximations of deep BNNs.
result DPEs achieve competitive performance with significantly less training data.
We present a method for training multi-label, massively multi-class image classification models, that is faster and more accurate than supervision via a sigmoid cross-entropy loss (logistic regression). Our method consists in embedding high-dimensional sparse labels onto a lower-dimensional dense sphere of unit-normed …
The paper investigates ethical issues in large image datasets, focusing on pornographic content.
problem Ethical issues in large-scale computer vision datasets, particularly concerning pornographic content.
method Cross-sectional model-based quantitative census covering various factors in the ImageNet-ILSVRC-2012 dataset.
result The dataset contains verifiably pornographic images, including non-consensual and voyeuristic content.
Machine Learning (ML) is increasingly being used for computer aided diagnosis of brain related disorders based on structural magnetic resonance imaging (MRI) data. Most of such work employs biologically and medically meaningful hand-crafted features calculated from different regions of the brain. The construction of su…
Novel LRMC tackles missing data and outliers in large-scale low-rank data recovery.
problem Missing data and extreme outliers in low-rank data analysis.
method Learned Robust Matrix Completion (LRMC) using deep unfolding and flexible neural network framework.
result LRMC achieves optimum performance with low computational complexity and linear convergence.
Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large scale screening is the inability to exhaustively detect fine blood vessels crucial to disease diagnosis. In this work we present a computational ima…
This paper presents a fast algorithm for clustering large datasets using Gaussian mixture models.
problem Efficiently clustering large-scale datasets with Gaussian mixture models.
method Variational EM algorithm with coreset objectives for sublinear complexity.
result Substantial speedups in clustering large-scale datasets (up to 32,000 clusters on 80 Million Tiny Images).
BigBiGAN improves unsupervised representation learning using image generation quality.
problem Improving unsupervised representation learning methods.
method Extending BigGAN to include an encoder and modifying the discriminator for representation learning.
result BigBiGAN models achieve state-of-the-art performance in unsupervised representation learning and unconditional image generation.
Modeling disease progression in brain images using monotonic Gaussian Processes.
problem Disentangling spatio-temporal disease trajectories from brain imaging data.
method Spatio-temporal matrix factorization with anatomically plausible priors, monotonic Gaussian Processes, and sparse codes.
result Monotonic Gaussian Processes model realistic disease trajectories in brain imaging data.
Self-guidance controls image generation by extracting properties from diffusion model representations.
problem Generating images from text descriptions is challenging due to the complexity of visual details.
method Self-guidance uses internal representations of diffusion models to control image generation.
result Properties like object shape, location, and appearance can be extracted and used to steer image generation.
Large-scale Hierarchical Classification (HC) involves datasets consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-sc…
Deep learning fails in classifying handwritten historical documents, traditional methods perform better.
problem Classifying handwritten historical documents using deep learning methods.
method Traditional and deep learning methods were tested on a large collection of handwritten historical manuscripts.
result Deep learning methods performed poorly, traditional methods performed consistently well.