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Found 18 papers, 7 papers with code
Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasets
In this paper, we emphasize that many datasets for AI-generated image detection contain biases related to JPEG compression and image size.
Estimating the Robustness of Classification Models by the Structure of the Learned Feature-Space
Over the last decade, the development of deep image classification networks has mostly been driven by the search for the best performance in terms of classification accuracy on standardized benchmarks like ImageNet.
Combining Transformer Generators with Convolutional Discriminators
Transformer models have recently attracted much interest from computer vision researchers and have since been successfully employed for several problems traditionally addressed with convolutional neural networks.
SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain
Despite the success of convolutional neural networks (CNNs) in many computer vision and image analysis tasks, they remain vulnerable against so-called adversarial attacks: Small, crafted perturbations in the input images can lead to false predictions.
Latent Space Conditioning on Generative Adversarial Networks
In particular, our approach exploits the structure of a latent space (learned by the representation learning) and employs it to condition the generative model.
Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches
In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings.
Local Facial Attribute Transfer through Inpainting
The term attribute transfer refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes.
Scalable Hyperparameter Optimization with Lazy Gaussian Processes
Reducing its computational complexity from cubic to quadratic allows an efficient strong scaling of Bayesian Optimization while outperforming the previous approach regarding optimization accuracy.
Unmasking DeepFakes with simple Features
In this work, we present a simple way to detect such fake face images - so-called DeepFakes.
Semi Few-Shot Attribute Translation
Recent studies have shown remarkable success in image-to-image translation for attribute transfer applications.
GradVis: Visualization and Second Order Analysis of Optimization Surfaces during the Training of Deep Neural Networks
Current training methods for deep neural networks boil down to very high dimensional and non-convex optimization problems which are usually solved by a wide range of stochastic gradient descent methods.
Object Segmentation using Pixel-wise Adversarial Loss
Recent deep learning based approaches have shown remarkable success on object segmentation tasks.
Stabilizing GANs with Soft Octave Convolutions
The basic idea of our approach is to split convolutional filters into additive high and low frequency parts, while shifting weight updates from low to high during the training.
Sparsity in Deep Neural Networks - An Empirical Investigation with TensorQuant
Various approaches have been investigated to reduce the necessary resources, one of which is to leverage the sparsity occurring in deep neural networks due to the high levels of redundancy in the network parameters.
TensorQuant - A Simulation Toolbox for Deep Neural Network Quantization
While most related publications validate the proposed approach on a single DNN topology, it appears to be evident, that the optimal choice of the quantization method and number of coding bits is topology dependent.
Using GPI-2 for Distributed Memory Paralleliziation of the Caffe Toolbox to Speed up Deep Neural Network Training
I/O is an other bottleneck to work with DDNs in a standard parallel HPC setting, which we will consider in more detail in a forthcoming paper.
Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability
This paper presents a theoretical analysis and practical evaluation of the main bottlenecks towards a scalable distributed solution for the training of Deep Neuronal Networks (DNNs).
Asynchronous Parallel Stochastic Gradient Descent - A Numeric Core for Scalable Distributed Machine Learning Algorithms
In this context, Stochastic Gradient Descent (SGD) methods have long proven to provide good results, both in terms of convergence and accuracy.
Distributed, Parallel, and Cluster Computing
