2.2k citations · 5.2k across the 82 of their papers we have counts for
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Modeling Conceptual Understanding in Image Reference Games
Rodolfo Corona, Stephan Alaniz, Zeynep Akata
An agent who interacts with a wide population of other agents needs to be aware that there may be variations in their understanding of the world. Furthermore, the machinery which t…
Understanding Misclassifications by Attributes
Sadaf Gulshad, Zeynep Akata, Jan Hendrik Metzen +1
In this paper, we aim to understand and explain the decisions of deep neural networks by studying the behavior of predicted attributes when adversarial examples are introduced. We…
Bayesian Zero-Shot Learning
Sarkhan Badirli, Zeynep Akata, Murat Dundar
Object classes that surround us have a natural tendency to emerge at varying levels of abstraction. We propose a Bayesian approach to zero-shot learning (ZSL) that introduces the n…
Relational Generalized Few-Shot Learning
Xiahan Shi, Leonard Salewski, Martin Schiegg +2
Transferring learned models to novel tasks is a challenging problem, particularly if only very few labeled examples are available. Although this few-shot learning setup has receive…
Combining Generative and Discriminative Models for Hybrid Inference
Victor Garcia Satorras, Zeynep Akata, Max Welling
A graphical model is a structured representation of the data generating process. The traditional method to reason over random variables is to perform inference in this graphical mo…
Interpreting Adversarial Examples with Attributes
Sadaf Gulshad, Jan Hendrik Metzen, Arnold Smeulders +1
Deep computer vision systems being vulnerable to imperceptible and carefully crafted noise have raised questions regarding the robustness of their decisions. We take a step back an…
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning
Yongqin Xian, Saurabh Sharma, Bernt Schiele +1
When labeled training data is scarce, a promising data augmentation approach is to generate visual features of unknown classes using their attributes. To learn the class conditiona…
Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval
Anjan Dutta, Zeynep Akata
Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer vision, allowing to retrieve natural images relevant to sketch queries that might not been seen in the…
Learning Decision Trees Recurrently Through Communication
Stephan Alaniz, Diego Marcos, Bernt Schiele +1
Integrated interpretability without sacrificing the prediction accuracy of decision making algorithms has the potential of greatly improving their value to the user. Instead of ass…
Visual Rationalizations in Deep Reinforcement Learning for Atari Games
Laurens Weitkamp, Elise van der Pol, Zeynep Akata
Due to the capability of deep learning to perform well in high dimensional problems, deep reinforcement learning agents perform well in challenging tasks such as Atari 2600 games.…