This study investigates how much knowledge from natural images can be transferred to pathology images.
problem Quantifying how much knowledge from natural images can be transferred to pathology images.
method Proposes a framework to quantify knowledge gain by a particular layer, conducts empirical investigation in pathology image centered transfer learning.
result Early layers of deep models can transfer knowledge to pathology image classification tasks.
Survey of deep learning methods for fMRI natural image reconstruction.
problem Reconstructing natural images from fMRI brain activity.
method Survey of deep learning approaches, including architectural design, datasets, and evaluation metrics.
result Performance evaluation across standardized metrics.
GANs can improve image reconstruction by using intermediate layers.
problem Improving the quality of image reconstruction using GANs.
method Exploiting the representation in intermediate layers of the generator.
result Intermediate layers in GANs can represent natural images with high visual fidelity.
Learning the distribution of natural images is one of the hardest and most important problems in machine learning. The problem remains open, because the enormous complexity of the structures in natural images spans all length scales. We break down the complexity of the problem and show that the hierarchy of structures …
Study extends cognitive modeling to natural images, revealing the importance of image representation.
problem Extending cognitive modeling to natural images and understanding human categorization.
method Conducted a large-scale study with over 500,000 human judgments. Used deep and shallow machine learning methods to represent images. Applied psychological models of categorization to natural images.
result Simple models with abstract prototypes outperform complex exemplar accounts when using expressive, data-driven image representations.
Spectral images captured by satellites and radio-telescopes are analyzed to obtain information about geological compositions distributions, distant asters as well as undersea terrain. Spectral images usually contain tens to hundreds of continuous narrow spectral bands and are widely used in various fields. But the vast…
Unified framework detects natural and adversarial errors in image classifications.
problem Detecting both unintentional and intentional errors in image classifications.
method Detects errors using invariance to image transformations.
result Our approach surpasses previous methods by a large margin.
We describe a model for capturing the statistical structure of local amplitude and local spatial phase in natural images. The model is based on a recently developed, factorized third-order Boltzmann machine that was shown to be effective at capturing higher-order structure in images by modeling dependencies among squar…
New method reconstructs images from fMRI data using unlabeled data.
problem Challenges in acquiring labeled data for fMRI-to-image reconstruction.
method Self-supervised training with Encoder-Decoder and Decoder-Encoder networks.
result Reconstruction network adapts to new unlabeled test data.
Generative models improve image probability estimation but lack interpretability.
problem Lack of interpretability in generative models for natural image distributions.
method Extracted explicit probability density estimates from GANs and analyzed latent representations.
result Natural image density functions are difficult to interpret.
Convolutional networks can denoise images without training data.
problem Denoising and regularization of images without labeled data.
method Exploiting the structural bias of convolutional generators through gradient descent.
result Early-stopped gradient descent denoises/regularizes images effectively.
Study reveals differences in medical image models' hidden representation refinement.
problem Understanding how intrinsic dimensionality changes in neural network hidden representations across different domains.
method Analysis of 11 natural and medical image datasets using 6 network architectures.
result Medical image models refine hidden representations earlier, suggesting differences in feature abstraction.
New method detects adversarial images by exploiting their density.
problem Detecting adversarial images that are hard to find with gradient methods.
method Develop a test based on the density of adversarial directions.
result Unprecedented accuracy in detecting adversarial attacks under white-box setting.
A new method compares image classifiers using adaptive sampling of natural images.
problem Evaluation of image classifiers on small, fixed test sets may not generalize to real-world images.
method Adaptive sampling from a large corpus of unlabeled images to maximize classifier discrepancies measured by WordNet hierarchy.
result Human labeling of model-dependent image sets reveals relative classifier performance.
We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high dimensional space. Unlike …
Survey of deep learning methods for image captioning.
problem Generating accurate and complex image descriptions.
method Comprehensive review of deep learning techniques for image captioning.
result Analysis of strengths, limitations, and popular datasets in deep learning image captioning.
Generates high-resolution images from low-resolution inputs.
problem Generating realistic images from low-resolution inputs.
method Latent Adversarial Generator (LAG) using perceptual loss.
result Samples of high-resolution images from low-resolution inputs.
Tensor networks improve generative modeling of natural images.
problem Exponential decay of correlation in Matrix Product States limits their use for complex data.
method Introduced Tree Tensor Networks (TTN) for 2D data, developed efficient learning and sampling algorithms.
result TTN outperforms Matrix Product States in keeping pixel correlations and log-likelihood scores.
Study assesses deep neural networks' robustness in mammogram images.
problem Understanding deep neural networks' robustness in mammogram images for breast cancer screening.
method Measuring sensitivity to four perturbations and analyzing low-pass filtering effects.
result Mammogram image classifiers are sensitive to perturbations similar to natural images, but low-pass filtering degrades clinically meaningful features.
Optimal square matrices for image approximation under translation and rotation.
problem Approximating images with translation and rotation invariant subspaces.
method Abstract harmonic analysis for constructing optimal square matrices.
result Optimal approximation of images with minimal quadratic error.
CollaGAN uses GANs to impute missing image data.
problem Missing data bias in applications requiring multiple inputs.
method Collaborative Generative Adversarial Network (CollaGAN) for multi-domain image imputation.
result CollaGAN produces higher quality imputed images than existing methods.
The ability to characterize the color content of natural imagery is an important application of image processing. The pixel by pixel coloring of images may be viewed naturally as points in color space, and the inherent structure and distribution of these points affords a quantization, through clustering, of the color i…
Study shows image classifiers struggle with temporal changes in videos.
problem Temporal robustness of image classifiers in videos.
method Constructed datasets and evaluated various classifiers and detection models.
result Median classification accuracy drops of 16-10 points in robust datasets.
The thesis introduces methods to use semantic hierarchy in image classification.
problem Limited work in training image classifiers with non-conventional external guidance.
method Injects label hierarchy knowledge into arbitrary classifiers and uses order-preserving embeddings for image classification.
result Both embedding-based models and CNN-classifiers with hierarchical information outperform a hierarchy-agnostic model.
Untrained neural networks can recover natural images from few measurements.
problem Recovering natural images from a small number of measurements.
method Gradient descent on un-trained convolutional neural networks.
result Untrained neural networks can approximate reconstruct signals/images from a near minimal number of random measurements.
The paper explores how neural networks generalize differently from natural and medical images.
problem Discrepancies in generalization error between natural and medical images.
method Established and empirically validated a generalization scaling law with respect to intrinsic dataset properties.
result Higher intrinsic 'label sharpness' of medical images leads to higher adversarial vulnerability.
This work proves intrinsic robustness bounds for natural image distributions.
problem Understanding the robustness of natural image distributions against adversarial attacks.
method Assumes natural image distributions are captured by conditional generative models and proves robustness bounds for classifiers.
result Shows a large gap between theoretical robustness limits and current state-of-the-art adversarial robustness.
Energy-based models can generate complex images by combining simpler concepts.
problem Generating natural images that satisfy complex logical combinations of concepts.
method Energy-based models combine probability distributions of simpler concepts to generate compositions.
result Energy-based models can generate images that satisfy conjunctions, disjunctions, and negations of concepts.
This paper generates natural-looking perturbations to fool classifiers.
problem Generating adversarial examples that mimic natural objects or signals.
method Employing generative adversarial networks and optimization algorithms.
result The approach can fool classification models in both image and audio domains.
Localized adversarial training improves image classifiers' robustness.
problem State-of-the-art image classifiers fail on carefully manipulated adversarial images.
method Developed a localized adversarial attack and used it to train a robust classifier.
result Localized adversarial training increases robustness against adversarial inputs.
Study improves breast lesion segmentation with limited in vivo data using simulated and natural images.
problem Challenges in automatic breast lesion segmentation due to limited annotated data.
method Pre-training a segmentation network on simulated and natural images, followed by fine-tuning with limited in vivo data.
result Fine-tuning improves dice score by 21% with as little as 19 in vivo images.
CNNs adapted for brain images predict age with improved accuracy.
problem Applying CNNs to brain images without considering their 3D structure.
method Two modifications to existing CNN architectures tailored for brain images.
result Achieved a mean absolute error (MAE) of 1.4 years compared to 1.6 years for a baseline.
Associating image regions with text queries has been recently explored as a new way to bridge visual and linguistic representations. A few pioneering approaches have been proposed based on recurrent neural language models trained generatively (e.g., generating captions), but achieving somewhat limited localization accu…
Study shows current image classification models lack robustness to real-world dataset shifts.
problem Robustness of current image classification models to natural distribution shifts in real datasets.
method Evaluation of 204 ImageNet models in 213 different test conditions.
result Little to no transfer of robustness from synthetic to natural distribution shifts.
We present a probabilistic model for natural images which is based on Gaussian scale mixtures and a simple multiscale representation. In contrast to the dominant approach to modeling whole images focusing on Markov random fields, we formulate our model in terms of a directed graphical model. We show that it is able to …
Neural network classifies breast cancer lesions using global and local image features.
problem Classifying breast cancer lesions in medical images with high resolution and small regions of interest.
method Proposes a neural network that combines global saliency maps and local patches for pixel-level saliency maps.
result Achieves radiologist-level performance in screening mammography interpretation.
We pursue an early stopping technique that helps Gaussian Restricted Boltzmann Machines (GRBMs) to gain good natural image representations in terms of overcompleteness and data fitting. GRBMs are widely considered as an unsuitable model for natural images because they gain non-overcomplete representations which include…
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…
Generates detailed fashion feedback from outfit images.
problem Creating informative and diverse fashion feedback from outfit images.
method Trained deep generative models with visual attention, then improved with Maximum Mutual Information objective function.
result Generated sentences are more diverse and detailed.
New model generates images by reversing heat equation, revealing disentanglement.
problem Image generation without considering image structure.
method Stochastically reverses the heat equation to generate images, using variational approximation.
result Emergent disentanglement of overall colour and shape in images.
Deep semi-supervised learning identifies tree species from natural images.
problem Identifying tree species in natural settings with limited labeled data.
method Two-fold approach using deep semi-supervised learning.
result Achieves 94.04% top-5 accuracy for leaves and 83.04% for bark.
Generative model uses captions to generate images, improving semantic understanding.
problem Complex image generation models require large datasets and intricate learning.
method Adapts captioning models to generate images, using learned sentence and frame vectors.
result Images generated from multiple captions better capture semantic meaning.
Generative model generates images with multiple object classes.
problem Generating images with multiple object classes.
method Conditional Deep Convolutional GAN architecture, stabilized against collapse.
result System generates diverse samples with inter-object relationships.
AR-GANs learn depth and DoF from unlabeled images using aperture rendering and focus cues.
problem Learning depth and DoF from unlabeled natural images with diverse viewpoints and shapes.
method Aperture rendering and focus cues to learn depth and DoF from unlabeled images.
result AR-GANs effectively learn depth and DoF from various datasets, including flower, bird, and face images.
Gaudy images help train deep neural networks with less data.
problem Training deep neural networks with limited real data from visual cortex neurons.
method Used high-contrast binarized natural images (gaudy images) to train DNNs.
result Reduced training data needed for accurate DNN predictions of visual cortex neuron responses.
Generative model creates realistic images with 3D understanding.
problem Lack of 3D understanding in existing image generation models.
method Disentangled 3D representation using shape, viewpoint, and texture.
result Generates more realistic images and enables 3D operations.
A new natural gradient accounts for correlated variational parameters in variational inference.
problem Traditional natural gradients fail to correct for correlations in variational inference.
method Construct a new natural gradient called the Variational Predictive Natural Gradient (VPNG).
result VPNG accounts for the relationship between model parameters and variational parameters.
Transfer learning offers little benefit in medical imaging tasks.
problem Understanding transfer learning's impact on medical imaging tasks.
method Evaluation of transfer learning on two large-scale medical imaging tasks.
result Simple, lightweight models perform comparably to ImageNet architectures.