Alle Publikationen
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2018
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(2018): Large Scale GAN Training for High Fidelity Natural Image Synthesis. Online verfügbar unter http://arxiv.org/pdf/1809.11096v2
Abstract: Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple "truncation trick," allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6.
2014
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(2014): Cross-cultural differences in processing of architectural ranking. Evidence from an event-related potential study. In: COGNITIVE NEUROSCIENCE 5 (1), S. 45-53
Abstract: Visual object identification is modulated by perceptual experience. In a cross-cultural ERP study we investigated whether cultural expertise determines how buildings that vary in their ranking between high and low according to the Western architectural decorum are perceived. Two groups of German and Chinese participants performed an object classification task in which high- and low-ranking Western buildings had to be discriminated from everyday life objects. ERP results indicate that an early stage of visual object identification (i.e., object model selection) is facilitated for high-ranking buildings for the German participants, only. At a later stage of object identification, in which object knowledge is complemented by information from semantic and episodic long-term memory, no ERP evidence for cultural differences was obtained. These results suggest that the identification of architectural ranking is modulated by culturally specific expertise with Western-style architecture already at an early processing stage.
2012
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(2012) : Building high-level features using large scale unsupervised learning In: Langford, John; Pineau, Joelle (Hg.): Proceedings of the 29th International Conference on Machine Learning: Edinburgh, Scotland, UK: Anderson, USA: Omnipress, S. 507-514. Online verfügbar unter http://arxiv.org/pdf/1112.6209v5
Abstract: We consider the problem of building high-level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200x200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bodies. Starting with these learned features, we trained our network to obtain 15.8% accuracy in recognizing 20,000 object categories from ImageNet, a leap of 70% relative improvement over the previous state-of-the-art.
Keywords: Bilderkennung, Computer Science - Learning, Gestalt, Katze, schema, Schematheorie, Youtube
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