30 citations · 30 across the 1 of their papers we have counts for
9 papers
Adversarial Text-to-Image Synthesis: A Review
Stanislav Frolov, Tobias Hinz, Federico Raue +2
With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area. It is a flexible and intuitive way fo…
Crossmodal Language Grounding in an Embodied Neurocognitive Model
Stefan Heinrich, Yuan Yao, Tobias Hinz +5
Human infants are able to acquire natural language seemingly easily at an early age. Their language learning seems to occur simultaneously with learning other cognitive functions a…
Improved Techniques for Training Single-Image GANs
Tobias Hinz, Matthew Fisher, Oliver Wang +1
Recently there has been an interest in the potential of learning generative models from a single image, as opposed to from a large dataset. This task is of practical significance,…
Semantic Object Accuracy for Generative Text-to-Image Synthesis
Tobias Hinz, Stefan Heinrich, Stefan Wermter
Generative adversarial networks conditioned on textual image descriptions are capable of generating realistic-looking images. However, current methods still struggle to generate im…
Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples
Marcus Soll, Tobias Hinz, Sven Magg +1
Adversarial examples are artificially modified input samples which lead to misclassifications, while not being detectable by humans. These adversarial examples are a challenge for…
Generating Multiple Objects at Spatially Distinct Locations
Tobias Hinz, Stefan Heinrich, Stefan Wermter
Recent improvements to Generative Adversarial Networks (GANs) have made it possible to generate realistic images in high resolution based on natural language descriptions such as i…