InceptionGCN improves disease prediction on graph data.
problem Improving disease prediction accuracy on graph data.
method Introduced a new spectral domain architecture with inception modules and varying kernel sizes.
result Significantly improved disease prediction results on two datasets.
Improves GAN performance without changing network architecture.
problem Discriminator fails to guide generator effectively.
method Cascading Rejection (CR) module for discriminator.
result Significantly improves GAN performance in terms of FID.
Novel unsupervised LSTM method for video frame prediction.
problem Video frame prediction with limited labeled data.
method Inception-based LSTM architecture.
result Inception-based LSTM outperforms convolutional LSTM and has better performance than Inception v2 LSTM.
Skip connections make adversarial examples more transferable in ResNets.
problem Transferability of adversarial examples in ResNets with skip connections.
method Skip Gradient Method (SGM) to craft more transferable adversarial examples.
result SGM improves transferability of adversarial examples in various DNNs.
Proposes a new method to improve target annotation in ATR.
problem Challenges in annotating automatic target recognition due to lack of labeled data.
method Hybrid contrastive learning and cycle-consistency-based transductive transfer learning (C3TTL) framework.
result Significantly lower Fréchet Inception Distance (FID) score and improved performance in annotating civilian and military vehicles, as well as ship targets.
A high-parallelism SNN improves feature learning efficiency and robustness.
problem Slow learning speed and limited learning capability in existing SNNs.
method Inspired by Inception modules, high-parallelism architecture, Vote-for-All decoding, adaptive repolarization mechanism.
result Superior performance and competitive accuracy compared to state-of-the-art unsupervised SNNs.
Inception v3 model classifies face shapes with high accuracy.
problem Face shape classification using deep learning.
method Retraining Inception v3 model on face shape dataset.
result Inception v3 outperforms other classifiers.
In this article, we mathematically study several GAN related topics, including Inception score, label smoothing, gradient vanishing and the -log(D(x)) alternative. --- An advanced version is included in arXiv:1703.02000 "Activation Maximization Generative Adversarial Nets". Please refer Section 6 in 1703.02000 for deta…
DRS uses a GAN discriminator to improve sampling accuracy.
problem Improving sampling accuracy in GANs.
method Rejection sampling using discriminator output.
result DRS improves Inception Score and FID on ImageNet.
Hyperbolic GANs improve image generation metrics.
problem Improving image generation quality in neural networks.
method Integrating hyperbolic layers into GAN architectures.
result Hyperbolic GANs achieve better metrics than Euclidean counterparts.
Improves GANs by incorporating class information with a multi-hinge loss.
problem Improving GANs to better respect class information.
method Proposes a multi-class generalization of the Hinge loss for GANs.
result Improves Inception Scores and Frechet Inception Distance on Imagenet.
Proposes Topology Distance for evaluating GANs.
problem Challenges in evaluating GANs' goodness.
method Builds Vietoris-Rips complex on image features and defines TD based on latent manifold comparisons.
result Demonstrates TD's superiority over existing metrics.
Generative model produces high-fidelity video samples.
problem Generating high-fidelity videos from complex datasets.
method Large GAN trained on Kinetics-600 dataset, using a computationally efficient discriminator.
result Achieved state-of-the-art metrics in video synthesis and prediction.
FastFID efficiently trains generative models with FID loss.
problem Efficiently training generative models with FID loss.
method Introduces FastFID to train generative models with FID as a loss function.
result Improves FID for GANs using FID as an additional loss.
Paper proposes evaluation methods for climate change illustrations.
problem Lack of metrics to compare realism in conditional generative models.
method Adapted and assessed several existing metrics, including FID.
result FID with Inception-V3 embeddings correlates best with human realism.
This study evaluates how adversarial examples transfer between different models.
problem Transferability of adversarial examples across models poses a threat to machine learning reliability.
method Evaluation of three adversarial attacks (FGSM, Basic Iterative Method, Carlini & Wagner) on two model classes (VGG and Inception). Use of specific parameters and metrics (L-Infinity clipping, SSIM) for assessment.
result Adversarial examples can be transferred between models, indicating a vulnerability in machine learning systems.
Improved GAN training speed and quality with FastGAN.
problem Slower convergence and less expressive models in GAN training.
method Adversarial training with FastGAN algorithm.
result Better generation quality with less training time.
Proposes qIS for quantum generative models, extending classical inception score.
problem Develop a metric to evaluate quantum generative models.
method Introduces qIS, relating quality to Holevo information of quantum channel.
result qIS enhances the quality of quantum generative models, showing physical limitations.
This paper uses MH algorithm to improve variational inference and GANs.
problem Improving sampling efficiency in Bayesian inference and GANs.
method Proposes learning an independent sampler to maximize MH acceptance rate, related to variational inference. Deduces GANs from MH perspective.
result Improves variational inference and GANs performance on real-world datasets.
Improved sampling quality with RBM-Flow and D-Flow models.
problem Efficient sampling of complex data distributions using invertible flows.
method Implement RBM-Flow and D-Flow models with discrete latent variables.
result Significant improvement in sampling quality over baseline models.
LOGAN optimizes GAN training by improving adversarial dynamics.
problem Training GANs is challenging due to delicate adversarial dynamics and potential divergence.
method Integrates natural gradient-based latent optimisation into CS-GAN.
result Significant improvement in GAN training performance, achieving state-of-the-art results.
Method learns moments for large implicit models, improving image generation quality.
problem Difficulty in defining and selecting moments for training large implicit models.
method Introduced moment network and used asymptotic theory to define and learn better moments.
result MoLM-trained generators outperform other methods in quality and diversity of generated images.
Satellite imagery improves house price prediction models.
problem Improving accuracy of housing price estimation models.
method Transfer learning from ImageNet-pretrained Inception-v3 model to satellite images.
result Achieved a 10% improvement in R-squared score.
FID misaligns with human judgment due to reliance on ImageNet classes.
problem FID metric's reliance on ImageNet classes causes discrepancies with human evaluation.
method Investigated and visualized the feature space of FID and its relation to ImageNet classes.
result Aligning histograms of Top-N ImageNet classifications can reduce FID without improving quality. Recent work (Pennington et al, 2017) suggests that controlling the entire distribution of Jacobian singular values is an important design consideration in deep learning. Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs). We find th…
End-to-end audio recognition system improves accuracy.
problem Improving accuracy in auditory object recognition.
method Proposes an end-to-end deep neural network with an 'inception nucleus' to learn features from raw waveforms.
result Bests current state-of-the-art approaches by 10.4 percentage points on Urbansound8k dataset.
We study the impact of input noise dimension on GAN performance.
problem Challenges in GANs like mode collapse and unstable training.
method Quantitative analysis using FID and IS on different image datasets.
result The optimal input noise dimension varies by dataset and architecture.
Transform classical network structures to graph CNN for better graph recognition.
problem Transforming classical network structures to graph CNN for better graph recognition.
method Review and introduce ResNet, Inception, and DenseNet into graph CNN, constructing G_ResNet, G_Inception, G_DenseNet.
result Demonstrated how different network structures work on graph CNN in the graph recognition task.
Class labels have been empirically shown useful in improving the sample quality of generative adversarial nets (GANs). In this paper, we mathematically study the properties of the current variants of GANs that make use of class label information. With class aware gradient and cross-entropy decomposition, we reveal how …
Proposes a GNN framework for multivariate time series forecasting.
problem Lack of exploiting latent spatial dependencies in multivariate time series forecasting.
method Automatically extracts graph structures from multivariate time series data, integrates external knowledge, and uses mix-hop and dilated inception layers for capturing dependencies.
result Outperforms state-of-the-art methods on 3 out of 4 benchmark datasets.
Deep generative models are powerful tools that have produced impressive results in recent years. These advances have been for the most part empirically driven, making it essential that we use high quality evaluation metrics. In this paper, we provide new insights into the Inception Score, a recently proposed and widely…
Paper discusses sliced generative models for improved sample discrimination.
problem Improving sample discrimination in generative models.
method One-dimensional slicing of AutoEncoder-based generative models, focusing on normality tests and classical distances.
result The second group of methods based on classical distances gives a faster decrease rate of Fréchet Inception Distance (FID).
Automatically evaluates image quality based on human judgment.
problem Difficulty in rigorously evaluating generated image quality.
method Generative model embeddings, human labels regression, and statistical matching.
result 66% accuracy in predicting human scores of image realism.
Unified and extended precision-recall metrics for generative models.
problem Evaluation of generative models using scalar metrics like FID and IS.
method Unified various precision-recall approaches under a common framework.
result Recovered entire precision-recall curves and exposed metric pitfalls.
Discriminator optimizes to approximate optimal transport for better image generation.
problem Improving the quality of generated images using GANs.
method Trains discriminator to optimize a lower bound of Wasserstein distance, approximating optimal transport.
result Trained discriminator improves inception score and FID metrics.
MFIN networks improve crypto trading with multiple features.
problem Selecting and processing multiple features for effective trading.
method End-to-end framework using Multi-Factor Inception Networks (MFINs).
result MFINs learn uncorrelated, higher-Sharpe strategies not captured by traditional factors.
Improved image classification accuracy on CIFAR-10 dataset.
problem Classifying images from the CIFAR-10 dataset with high accuracy.
method Combining features from manual and deep learning approaches, including VGG16, Inception ResNet v2, HOG, and pixel intensities.
result Achieved 94.6% testing accuracy by combining top 1000 principal components.
New method evaluates text-to-image synthesis for realism, variety, and semantic accuracy.
problem Lack of metrics revealing semantic accuracy in text-to-image synthesis.
method Uses Inception network representations and t-SNE visualization for semantic evaluation.
result Classification accuracy of generated images to real images' visual concepts correlates with semantic accuracy.
Paper tackles GAN instability in audio and speech signals using a new similarity metric.
problem Improving stability of LS-GANs for audio and speech signals.
method Proposes a new similarity metric in unitary space of Schur decomposition for 2D audio and speech representations.
result Enhanced stability in training with less mode collapse compared to baseline GANs.
Spider GAN accelerates GAN training with a new approach.
problem Stable training of Generative adversarial networks (GANs).
method Spider GAN leverages a novel approach to identify closely related datasets (friendly neighborhoods) and uses a new measure (signed inception distance) to accelerate GAN training.
result Spider GAN achieves faster convergence and state-of-the-art FID values with one-fifth of the training iterations.
Image classification system identifies bumble bee species from images.
problem Manual identification of bumble bee species is time-consuming and requires expert knowledge.
method Transfer learning using Inception, VGG16, VGG19, and ResNet models.
result Inception and VGG classifiers achieved up to 23% accuracy for single species identification.
BigGAN achieves state-of-the-art image synthesis on ImageNet.
problem Generating high-fidelity images from complex datasets like ImageNet.
method Trained a large-scale GAN with orthogonal regularization and a truncation trick.
result Improved Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4.
Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data. Here we describe EEG-GAN as a framework to generate electroencephalographic (EEG) brain signals. We introduce a modification to the improved training of Wasserst…
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In …
TSGAN models time series data with limited data, outperforming competitors.
problem Lack of quality time series data for machine learning.
method Proposes Time Series GAN (TSGAN) for synthetic time series generation.
result TSGAN outperforms competitors in FID metric and qualitative classification.
Jeffrey guidance extends diffusion-model control to more complex applications.
problem Controlling diffusion models beyond simple cases like conditional sampling.
method Leveraging Jeffrey's rule of conditioning to update marginal distributions towards a target distribution.
result Significant reductions in FID on CIFAR-10 and FFHQ with Inception embeddings as the target.
Improved GAN performance with a novel local attention mechanism.
problem Enhancing the performance of Generative Adversarial Networks (GANs).
method Introducing a two-dimensional local attention mechanism that preserves geometry and locality, and using information flow graphs for design.
result Significant improvements in FID and Inception scores, from 18.65 to 15.94 on ImageNet.
We introduce Primal-Dual Wasserstein GAN, a new learning algorithm for building latent variable models of the data distribution based on the primal and the dual formulations of the optimal transport (OT) problem. We utilize the primal formulation to learn a flexible inference mechanism and to create an optimal approxim…