New method reduces neural image compression run-time by 50%.
problem Computational efficiency of neural image compression models.
method Automatic network optimization to reduce decoder complexity.
result Decreased decoder run-time by over 50%.
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48 results for “neural image compression”
Proposes new terms for neural image compression to improve quality and efficiency.
problem Improving the quality and efficiency of neural image compression.
method Introduces a compression objective and a cycle loss term, applied to autoencoder encoder outputs, combined with reconstruction losses.
result Different autoencoders trained with varying losses produce images with distinct perceptual qualities and image-domain distortions.
NICE framework explains CNN predictions and compresses images.
problem Interpreting deep neural networks and efficient image compression.
method End-to-end Neural Image Compression and Explanation (NICE) framework.
result NICE achieves high compression rates while maintaining classification accuracy.
C3 compresses images and videos with low complexity and high performance.
problem High complexity and low performance in neural compression models.
method Overfits a small model to each image or video separately, improving RD performance with low complexity.
result Matches the RD performance of state-of-the-art neural and video codecs with significantly lower decoding complexity.
In this paper, we present a novel approach for fine-tuning a decoder-side neural network in the context of image compression, such that the weight-updates are better compressible. At encoder side, we fine-tune a pre-trained artifact removal network on target data by using a compression objective applied on the weight-u…
Improved neural image compression with refined latent representations.
problem Sub-optimal results from variational autoencoders due to imperfect optimization and capacity limitations.
method Stochastic Gumbel Annealing (SGA) and its extensions (SGA+), including three different methods.
result Significant improvement in compression performance, especially on the R-D trade-off.
New algorithm uses untrained neural networks for image recovery, offering better compression.
problem Using untrained neural networks for image recovery and theoretical guarantees.
method Projected gradient descent scheme for solving linear and non-linear inverse problems.
result The method achieves better compression rates for the same image quality compared to hand-crafted priors.
NeLLoC improves image compression with parallel decoding.
problem Image compression with OOD generalization.
method Local autoregressive model with parallel decoding.
result Significant gains in compression runtime.
Convolutional neural networks can regularize inverse problems without training.
problem Solving inverse problems like image recovery from limited data.
method Fixed or parameterized convolutional networks with few parameters.
result Untrained convolutional networks can recover images from few measurements.
In this work, we propose an end-to-end block-based auto-encoder system for image compression. We introduce novel contributions to neural-network based image compression, mainly in achieving binarization simulation, variable bit rates with multiple networks, entropy-friendly representations, inference-stage code optimiz…
RandNet learns from compressed image data, improving efficiency and accuracy.
problem Efficiency and accuracy in training neural networks with large datasets.
method RandNet uses compressed random measurements of images to train neural networks efficiently.
result RandNet achieves comparable accuracy to full data training with minimal loss.
End-to-end meta-learned system for image compression.
problem Reducing the gap between training and inference conditions in image compression.
method Model-Agnostic Meta-learning approach for latent tensor overfitting and updating encoder and decoder networks.
result Meta-learned system achieves better compression performance compared to traditional methods.
This paper proposes a new method for efficient data compression using Bayesian neural networks.
problem Efficient compression of data represented as functions mapping coordinates to signal values.
method Overfitting variational Bayesian neural networks to the data and compressing an approximate posterior weight sample using relative entropy coding.
result Our method achieves strong performance on image and audio compression while retaining simplicity.
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.
This paper uses diffusion models for lossy image compression, improving perceptual metrics and practicality.
problem Lossy image compression with improved perceptual metrics and practicality.
method End-to-end optimized lossy image compression using conditional diffusion models.
result The model yields stronger FID scores and competitive performance in distortion metrics.
New method improves image compression using bits-back coding.
problem Lossy image compression with deep latent variable models.
method Iterative inference, stochastic annealing, bits-back coding.
result New state-of-the-art performance on lossy image compression.
Single model corrects JPEG artifacts for various compression settings.
problem JPEG compression artifacts due to aggressive quantization.
method Parameterized architecture using quantization matrix.
result State-of-the-art performance across different quality settings.
Compression affects deep networks differently, impacting underrepresented data points.
problem Disparate impact of compression on different classes and images.
method Analysis of deep neural network pruning and quantization effects.
result Compression disproportionately impacts model performance on underrepresented data points.
COIN++ compresses multiple data types efficiently.
problem Handling diverse data modalities in neural compression.
method Implicit neural representations and modulations quantization.
result Significant compression gains with reduced encoding time.
New hierarchical VQ-VAE scheme improves image compression quality and features at low bitrates.
problem Low bitrate image compression maintaining quality and features.
method Hierarchical VQ-VAE with stochastic quantization and Markovian latent variables.
result High perceptual quality and semantic features at low bitrates.
Cloud based medical image analysis has become popular recently due to the high computation complexities of various deep neural network (DNN) based frameworks and the increasingly large volume of medical images that need to be processed. It has been demonstrated that for medical images the transmission from local to clo…
The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy. Here, we propose an end-to-end, deep generative modeling approach to compress temporal sequences with a focus on video. Our approach builds upon…
Paper designs a lossy compression method for lossless prediction.
problem Ensuring high performance on predictive tasks with minimal data.
method Characterizes bit-rate requirements for invariant transformations, designs unsupervised objectives for neural compressors.
result Achieves substantial rate savings on ImageNet compared to JPEG without compromising classification performance.
This work combines GANs and A3C for high-resolution image compression.
problem Image compression for high-resolution images without loss of quality.
method Hybrid approach using GANs and A3C for end-to-end learning.
result Improves PSNR for high-resolution images through end-to-end learning.
Object detection in still images has drawn a lot of attention over past few years, and with the advent of Deep Learning impressive performances have been achieved with numerous industrial applications. Most of these deep learning models rely on RGB images to localize and identify objects in the image. However in some a…
Improved hybrid image compression using deep learning and traditional codecs.
problem Efficiently compressing images with high quality and low bitrates.
method Combining CNN for compact representation, FLIF for base layer, and BPG for enhancement layer.
result Outperforms state-of-the-art schemes in PSNR and MS-SSIM metrics.
This paper compresses neural networks by permuting and quantizing weights.
problem Efficiently compressing large neural networks for resource-constrained platforms.
method Permuting and quantizing weights, connecting to rate-distortion theory, and using annealed quantization.
result Significant compression with minimal accuracy loss, e.g., 40-70% reduction in gap with uncompressed model.
WildCat efficiently compresses neural network attention mechanisms.
problem Expensive quadratic runtime of attention mechanisms in neural networks.
method Uses a weighted coreset with a fast subsampling algorithm to approximate attention with near-linear time complexity.
result Approximates exact attention with super-polynomial error decay and near-linear runtime.
A novel generative encoder model for imaging and image processing.
problem Efficiently processing and recovering images with noise.
method A pre-training phase with a GAN and an AE, followed by an optimization phase.
result The GE model outperforms state-of-the-art algorithms in image recovery.
New DNN boosts image retrieval efficiency.
problem Efficient compression of visual descriptors for large-scale image retrieval.
method Unsupervised multi-codebook quantization in a DNN architecture.
result Significantly outperforms existing methods on visual descriptor datasets.
Improved image recovery with minimal data using untrained neural networks.
problem Solving inverse problems with limited data.
method Pre-training neural networks with a small number of examples to improve performance.
result Performance increases as data increases, matching generative models with less than 1% of training data.
This study presents a new lossy image compression method that utilizes the multi-scale features of natural images. Our model consists of two networks: multi-scale lossy autoencoder and parallel multi-scale lossless coder. The multi-scale lossy autoencoder extracts the multi-scale image features to quantized variables a…
TensorShield defends images from adversarial attacks using tensor decomposition.
problem Adversarial attacks on images can fool deep neural networks.
method Tensor decomposition to find low-rank approximations of images, reducing high-frequency perturbations.
result TensorShield outperforms existing methods like SLQ by 14% against FGSM attacks.
Modality-agnostic compression improves across diverse data types.
problem Efficiently compressing data across multiple modalities.
method Functional view of data, Implicit Neural Representation (INR), modality-agnostic latent representations, variational compression.
result Improved performance compared to existing methods, especially for diverse modalities.
A new method uses compressive autoencoders for image restoration.
problem Efficient regularization of inverse problems in computational imaging.
method Variational Bayes Latent Estimation (VBLE) with compressive autoencoders.
result VBLE achieves similar performance to state-of-the-art PnP methods but faster.
A new method, REC, compresses images by encoding their latent representations efficiently.
problem Efficiently compressing single images with latent representations.
method Relative Entropy Coding (REC) that directly encodes latent representations with codelength close to relative entropy.
result REC is more efficient for single image compression compared to previous methods and is competitive for lossy compression.
Deep networks learn to compress high-dimensional data into lower dimensions.
problem Learning to identify and extract task-relevant variables from high-dimensional data.
method Applied state-of-the-art techniques for intrinsic dimensionality estimation.
result Neural networks first expand dimensionality in initial layers, then compress in later layers.
A new algorithm for compressing latent representations in deep models.
problem Compressing continuous latent representations in deep models.
method Separates model design and training from quantization; uses adaptive quantization based on posterior uncertainty.
result Image compression with the proposed algorithm outperforms JPEG over a wide range of bit rates.
Neural Estimation of the Rate-Distortion Function With Applications to Operational Source Codingcs.IT
Estimates rate-distortion function for large datasets using neural networks.
problem Designing lossy data compression schemes and comparing them with theoretical limits.
method Re-formulate rate-distortion objective and solve using neural networks.
result NERD accurately estimates the rate-distortion function for real-world datasets.
This paper improves the scalability of sparse neural network compression.
problem Sparse neural network compression for diverse data modalities.
method State-of-the-art sparsification techniques and meta-learning.
result Meta-learning sparse compression networks achieve new state-of-the-art results.
ResNet learns to compress information during training.
problem Applying information bottleneck principle to deep learning models.
method Trained PixelCNN++ models to measure mutual information between ResNet hidden layers and input data.
result Compression occurs in both classification and autoencoding training regimes.
DNNs can approximate fractal functions with exponential linear regions.
problem Understanding neural network approximations of complex functions.
method Using Iterated Function Systems (IFS) and neural networks to generate fractal functions.
result DNNs can generate fractal functions with a number of linear regions exponential in the number of parameters.
HiLLoC compresses large images losslessly using VAEs.
problem Lossless compression of large color photographs.
method Fully convolutional VAE models trained on ImageNet are applied to lossless compression.
result Achieves state-of-the-art compression for full-size ImageNet images.
Pyramid Attention Networks improve image restoration by leveraging self-similarities across scales.
problem Lack of full exploitation of self-similarities in image restoration by recent deep learning methods.
method Introduces a Pyramid Attention module that processes multi-scale feature correspondences to borrow clean signals from coarser levels.
result Pyramid Attention module achieves state-of-the-art results in various image restoration tasks.
Deep neural networks, in particular convolutional neural networks, have become highly effective tools for compressing images and solving inverse problems including denoising, inpainting, and reconstruction from few and noisy measurements. This success can be attributed in part to their ability to represent and generate…
Asymptotic Optimism for Tensor Regression Models with Applications to Neural Network Compressionstat.ML
Optimizes tensor rank selection for neural network compression.
problem Finding optimal tensor rank for regression models.
method Analyzes population expressions for training-testing discrepancy under Gaussian design.
result Optimal rank minimizes prediction error and aligns with cross-validation.
Deep neural networks (DNNs) have achieved significant success in a variety of real world applications, i.e., image classification. However, tons of parameters in the networks restrict the efficiency of neural networks due to the large model size and the intensive computation. To address this issue, various approximatio…
Turbo-DDCM speeds up zero-shot image compression.
problem Slowness and high computational demand in zero-shot diffusion-based compression.
method Modified DDCM framework with Turbo-DDCM, combining noise vectors and improved encoding.
result Turbo-DDCM achieves faster compression than state-of-the-art methods.