New approach corrects image bias without labels.
problem Image search results skew towards majority groups.
method Uses visibly diverse control set to select images.
result Significantly improves visible diversity of results.
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169,051 papers · 148 categories
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48 results for “Image Diversity”
Improves diversity of text-to-image models without sacrificing FID.
problem Lack of diversity and tendency to recreate training set images.
method Adds sparse repellency terms to diffusion SDE to guide trajectories away from a reference set.
result Improves diversity of diffusion models with minimal impact on FID.
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.
A method to improve image synthesis diversity using mutual information.
problem Mode collapse in conditional GANs for multimodal image synthesis.
method Explicitly estimate and maximize mutual information between latent code and output image.
result Prevents mode collapse and encourages synthesis of diverse images.
Paper presents a deterministic method for diverse subset selection.
problem Diverse subset selection problems in recommendation, summarization, and search.
method Greedy deterministic adaptation of k-DPP for low-rank approximations and image search.
result The method yields low-rank approximations of kernel matrices and demonstrates effectiveness in image search.
Improves generative Visual Dialog by asking diverse questions.
problem Generative Visual Dialog models degrade after a few rounds of interaction.
method Introduce a simple auxiliary objective to incentivize Qbot to ask diverse questions.
result Better dialog diversity, consistency, fluency, and detail with improved image relevance.
Proposes a method to generate diverse outputs in conditional GANs.
problem Mode-collapse in conditional GANs, where outputs are overly simplified.
method Explicit regularization to produce diverse outputs based on latent codes.
result Demonstrates improved diversity in image-to-image translation, inpainting, and future video prediction tasks.
We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. First, MAD-GAN is a multi-agent GAN architecture incorporating multiple generators and one discriminator. Second, to enforce that different gener…
The paper teaches a reinforcement learning agent to generate diverse programs based on symbolic instructions.
problem Learning to generate diverse programs for diverse scenes given a symbolic instruction.
method Instruction-conditioned reinforced adversarial learning.
result The agent's stochastic policy more accurately captures the diversity in the goal distribution.
Lung segmentation accuracy varies little across diverse datasets.
problem Limited clinical applicability of automated lung segmentation methods.
method Comparison of four deep learning approaches and two standard algorithms on diverse datasets.
result Standard U-net approach yields higher accuracy on routine imaging data.
Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 r…
Enhanced VQ-VAE generates high-fidelity images faster.
problem Generating high-fidelity images efficiently.
method Scaled VQ-VAE with fast autoregressive sampling.
result VQ-VAE generates samples with quality rivaling GANs.
NegToMe uses images to guide text-based models away from unwanted visual elements.
problem Insufficient text-based adversarial guidance for complex visual concepts.
method Negative token merging (NegToMe) using visual features from reference images.
result Significantly enhances output diversity and reduces visual similarity to copyrighted content.
Generates diverse images by resampling specific parts while maintaining global consistency.
problem Creating diverse images while maintaining global consistency in certain parts.
method Developed a new network architecture, training procedure, and resampling algorithm.
result Achieved low distortion block-resampling with spatially stochastic networks.
TAC-GAN improves image diversity in AC-GAN by minimizing class distribution divergence.
problem Low diversity in AC-GAN's generated samples as class count increases.
method TAC-GAN introduces twin auxiliary classifiers to address class separability issues.
result TAC-GAN effectively minimizes divergence between generated and real data distributions.
New method designs joint initial noises for diffusion models to improve diversity and alignment.
problem Independent initial noises limit diversity in generated images.
method Coupling of initial noises, maintaining Gaussian distribution while allowing dependence.
result Repulsive Gaussian coupling improves diversity without increasing sampling cost.
Improves GANs by enforcing diverse feature learning.
problem GANs can collapse to a single configuration and be unstable.
method Enforces diverse feature learning by penalizing correlated features.
result Enforces diverse features, stabilizes training, and improves image synthesis.
Seq-CVAE learns a latent space for each word position to capture sentence intention.
problem Capturing diversity in image captioning models.
method Seq-CVAE learns a sequential latent space for each word position, mimicking future sentence summaries.
result Significantly improves diversity metrics on MSCOCO dataset compared to baselines.
A new method models uncertainty in medical image segmentation.
problem Inherent ambiguity in anatomical structure and pathology segmentation.
method Hierarchical probabilistic model with variational autoencoder.
result Generates more realistic and diverse segmentation samples.
Strong baseline for medical imaging domain adaptation.
problem Improving generalization in medical imaging across different datasets.
method Training on diverse chest X-ray datasets.
result Empirical demonstration of model generalization to out-of-sample domains.
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.
SASSL improves self-supervised learning by preserving image structure.
problem Distorted augmented samples in self-supervised learning.
method Neural Style Transfer to decouple semantic and stylistic attributes.
result Boosts ImageNet top-1 accuracy by up to 2 percentage points.
New metrics improve reliability of image generation evaluation.
problem Lack of reliable metrics for evaluating fidelity and diversity in generative models.
method Proposed density and coverage metrics to diagnose fidelity and diversity separately.
result Density and coverage metrics provide more interpretable and reliable signals.
Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In this work, we aim to model a \emph{distribution} of possible outputs in a conditional generative modeling setting. The ambiguity of the mapping is distilled in a low-dimensional latent vector,…
Generative Fractional Diffusion Models improve image diversity and quality.
problem Diffusion models struggle with diversity, mode-collapse, and slow convergence.
method Replaces light-tailed BM with fractional Brownian motion (fBM) and its Markov approximation (MA-fBM).
result GFDM achieves greater diversity and quality in image generation.
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.
Fine-tunes diffusion models to generate diverse samples with high genuine rewards.
problem Reward collapse in finetuning diffusion models.
method Entropy-regularized control against pretrained diffusion models.
result Efficient generation of diverse samples with high genuine rewards.
This paper explores neural networks for colorizing grayscale images.
problem Colorizing grayscale images using neural networks.
method Comparison of existing and novel generative models (CVAE, CWGAN-GP, AGE, IVAE) trained on CIFAR-10 images.
result CVAE with L1 reconstruction loss and IVAE achieve the highest Inception Score (IS).
Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous Variablecs.CV
This work introduces an efficient method to sample high-quality images from conditional GANs.
problem Efficient subsampling of images from conditional GANs (cGANs) is challenging.
method Developed a novel conditional density ratio estimation method (cDRE-F-cSP) and rejection sampling scheme (cDR-RS).
result cDR-RS outperforms state-of-the-art methods in both effectiveness and efficiency.
Generative Adversarial Networks (GANs) have been shown to produce realistically looking synthetic images with remarkable success, yet their performance seems less impressive when the training set is highly diverse. In order to provide a better fit to the target data distribution when the dataset includes many different…
GANs simulate realistic galaxy images.
problem Simulate complex astronomical images efficiently.
method Progressive GANs with Wasserstein cost function.
result Generates naturalistic galaxy images.
Few-step distillation improves T2I models without real images or CFG trade-offs.
problem Challenges in accelerating T2I models with high-resolution and CFG.
method Score identity distillation (SiD) for few-step generation, with adversarial loss and new guidance strategies.
result State-of-the-art performance on SDXL at 1024x1024 resolution, robust to real images absence.
URT layer improves few-shot image classification across diverse domains.
problem Few-shot image classification in multi-domain settings.
method Meta-learns to dynamically re-weight and compose domain-specific representations.
result Sets new state-of-the-art on Meta-Dataset.
Modern machine learning systems such as image classifiers rely heavily on large scale data sets for training. Such data sets are costly to create, thus in practice a small number of freely available, open source data sets are widely used. We suggest that examining the geo-diversity of open data sets is critical before …
Semantic inpainting is the task of inferring missing pixels in an image given surrounding pixels and high level image semantics. Most semantic inpainting algorithms are deterministic: given an image with missing regions, a single inpainted image is generated. However, there are often several plausible inpaintings for a…
Improved RL for grasping in cluttered scenes using state representation learning.
problem Poor performance of RL methods in grasping diverse objects from raw images.
method Employed state representation learning (SRL) with disentanglement of raw input images.
result Deep RL can learn grasping skills from varied visual inputs.
CAFLOW uses auto-regressive flows to translate images efficiently.
problem Image-to-image translation tasks.
method Transforms conditioning image into latent encodings using normalizing flows, models conditional distribution with auto-regressive distributions.
result Outperforms former conditional flow designs.
We present a framework to understand GAN training as alternating density ratio estimation and approximate divergence minimization. This provides an interpretation for the mismatched GAN generator and discriminator objectives often used in practice, and explains the problem of poor sample diversity. We also derive a fam…
MOD improves ensemble-based uncertainty estimates by encouraging larger diversity.
problem Improving model uncertainty estimates for inputs not seen during training.
method Maximize Overall Diversity (MOD) approach to encourage larger diversity in ensemble predictions.
result Significantly improves predictive performance for out-of-distribution test examples.
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.
Framework improves agent's ability to learn from noisy images.
problem Agents tend to focus on distracting regions in unsupervised image-based goal exploration.
method Proposes a novel framework combining absolute Learning Progress with unsupervised image-based goal exploration.
result Agents successfully identify and ignore distracting regions, improving overall performance.
New measures quantify diversity of latent representations using metric space magnitude.
problem Evaluating the diversity of latent representations in machine learning models.
method Developed magnitude-based measures for latent representations, stable under data perturbations.
result Demonstrated superior performance across various domains and tasks.
Single-image super-resolution (SISR) is a canonical problem with diverse applications. Leading methods like SRGAN produce images that contain various artifacts, such as high-frequency noise, hallucinated colours and shape distortions, which adversely affect the realism of the result. In this paper, we propose an altern…
Paper introduces FJD metric for cGAN benchmarking.
problem Quantitative evaluation of cGANs using multiple metrics.
method Frechet Joint Distance (FJD) metric.
result FJD provides a single metric for cGAN benchmarking and model selection.
Generative Adversarial Networks (GANs) have a great performance in image generation, but they need a large scale of data to train the entire framework, and often result in nonsensical results. We propose a new method referring to conditional GAN, which equipments the latent noise with mixture of Student's t-distributio…
Novel unsupervised method for fast 3D image registration using cycle-consistent CNN.
problem Medical image registration for cancer diagnosis.
method Unsupervised deep learning using cycle-consistent CNN for deformable registration.
result Very precise 3D image registration within a few seconds, improving cancer size estimation.
Deep learning has significant potential for medical imaging. However, since the incident rate of each disease varies widely, the frequency of classes in a medical image dataset is imbalanced, leading to poor accuracy for such infrequent classes. One possible solution is data augmentation of infrequent classes using syn…
Enhances curve alignment for diverse data types.
problem Aligning curve data effectively.
method Developed nonlinear transformations for curve data.
result Successfully aligned synthetic and real curve data.