Daniel Gläsner
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View article: LatentCRF: Continuous CRF for Efficient Latent Diffusion
LatentCRF: Continuous CRF for Efficient Latent Diffusion Open
Latent Diffusion Models (LDMs) produce high-quality, photo-realistic images, however, the latency incurred by multiple costly inference iterations can restrict their applicability. We introduce LatentCRF, a continuous Conditional Random Fi…
View article: Rethinking FID: Towards a Better Evaluation Metric for Image Generation
Rethinking FID: Towards a Better Evaluation Metric for Image Generation Open
As with many machine learning problems, the progress of image generation methods hinges on good evaluation metrics. One of the most popular is the Frechet Inception Distance (FID). FID estimates the distance between a distribution of Incep…
View article: MarkovGen: Structured Prediction for Efficient Text-to-Image Generation
MarkovGen: Structured Prediction for Efficient Text-to-Image Generation Open
Modern text-to-image generation models produce high-quality images that are both photorealistic and faithful to the text prompts. However, this quality comes at significant computational cost: nearly all of these models are iterative and r…
View article: On the Effectiveness of Impedance-Based Fingerprint Presentation Attack Detection
On the Effectiveness of Impedance-Based Fingerprint Presentation Attack Detection Open
Within the last few decades, the need for subject authentication has grown steadily, and biometric recognition technology has been established as a reliable alternative to passwords and tokens, offering automatic decisions. However, as uns…
View article: Balancing Robustness and Sensitivity using Feature Contrastive Learning
Balancing Robustness and Sensitivity using Feature Contrastive Learning Open
It is generally believed that robust training of extremely large networks is critical to their success in real-world applications. However, when taken to the extreme, methods that promote robustness can hurt the model's sensitivity to rare…
View article: Balancing Constraints and Submodularity in Data Subset Selection.
Balancing Constraints and Submodularity in Data Subset Selection. Open
Deep learning has yielded extraordinary results in vision and natural language processing, but this achievement comes at a cost. Most deep learning models require enormous resources during training, both in terms of computation and in huma…
View article: Less is more: Selecting informative and diverse subsets with balancing constraints
Less is more: Selecting informative and diverse subsets with balancing constraints Open
Deep learning has yielded extraordinary results in vision and natural language processing, but this achievement comes at a cost. Most models require enormous resources during training, both in terms of computation and in human labeling eff…
View article: Understanding Robustness of Transformers for Image Classification
Understanding Robustness of Transformers for Image Classification Open
Deep Convolutional Neural Networks (CNNs) have long been the architecture of choice for computer vision tasks. Recently, Transformer-based architectures like Vision Transformer (ViT) have matched or even surpassed ResNets for image classif…
View article: Half-occlusion boundary detectors in computational stereo vision
Half-occlusion boundary detectors in computational stereo vision Open
There are two sources of depth information in a stereo pair. One is the correlation signal from smooth surface regions that are visible to both eyes, which provides depth information via triangulation. The other is the decorrelation signal…
View article: A dynamic programming algorithm for perceptually consistent stereo
A dynamic programming algorithm for perceptually consistent stereo Open
This document provides details of the dynamic programming algorithm discussed in “Towards perceptually consistent stereo: a scanline study." For the motivation of the algorithm, see that paper.