GANs for All: Supporting Fun and Intuitive Exploration of GAN Latent Spaces

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Organocatalysis has evolved significantly over the last decades, becoming a pillar of synthetic chemistry, but traditional theoretical approaches based on quantum mechanical computations to investigate reaction mechanisms and provide rationalizations of ca ...

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Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolut ...

One prominent approach toward resolving the adversarial vulnerability of deep neural networks is the two-player zero-sum paradigm of adversarial training, in which predictors are trained against adversarially-chosen perturbations of data. Despite the promi ...

Test time augmentation has been shown to be an effective approach to combat domain shifts in deep learning. Despite their promising performance levels, the interpretability of the underlying used models is however low. Saliency maps have been widely used i ...

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Text-to-image models, such as Stable Diffusion, can generate high-quality images from simple textual prompts. With methods such as Textual Inversion, it is possible to expand the vocabulary of these models with additional concepts, by learning the vocabula ...

GANs for All: Supporting Fun and Intuitive Exploration of GAN Latent Spaces

Chat with Graph Search

Dense Image-based Predictions for Comics Analysis

Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

Weakly supervised joint whole-slide segmentation and classification in prostate cancer

Enriching the Computational Toolbox for Organocatalysis

Scale-Aware Test-Time Click Adaptation for Pulmonary Nodule and Mass Segmentation

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

Confidence Matters: Applications to Semantic Segmentation

Supervised learning and inference of spiking neural networks with temporal coding

Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups

Multi-site, Multi-domain Airway Tree Modeling

Adversarial Training Should Be Cast As a Non-Zero-Sum Game

SAGTTA: SALIENCY GUIDED TEST TIME AUGMENTATION FOR MEDICAL IMAGE SEGMENTATION ACROSS VENDOR DOMAIN SHIFT

Detecting Anomalies and Obstacles in Road Scenes

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text