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Online-Trained Upsampler for Deep Low Complexity Video Compression
This hybrid solution has a small scope of only 10s or 100s of frames and allows for a low complexity both on the encoding and the decoding side.
How to Exploit the Transferability of Learned Image Compression to Conventional Codecs
Recent research suggests that generative adversarial networks have the ability to overcome this limitation and serve as a multi-modal loss, especially for textures.
Joint Pruning & Quantization for Extremely Sparse Neural Networks
We investigate pruning and quantization for deep neural networks.
Utilising Low Complexity CNNs to Lift Non-Local Redundancies in Video Coding
The method can be applied to any existing video codec to increase coding gains while its low computational footprint allows for an application under resource-constrained conditions.
What Synthesis is Missing: Depth Adaptation Integrated with Weak Supervision for Indoor Scene Parsing
Our proposed two-stage integration more than halves the gap towards fully supervised methods when compared to previous state-of-the-art in transfer learning.
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