111 citations · 462 across the 65 of their papers we have counts for
8 papers · 1 filter
Deep motion estimation for parallel inter-frame prediction in video compression
André Nortje, Herman A. Engelbrecht, Herman Kamper
Standard video codecs rely on optical flow to guide inter-frame prediction: pixels from reference frames are moved via motion vectors to predict target video frames. We propose to…
BINet: a binary inpainting network for deep patch-based image compression
André Nortje, Willie Brink, Herman A. Engelbrecht +1
Recent deep learning models outperform standard lossy image compression codecs. However, applying these models on a patch-by-patch basis requires that each image patch be encoded a…
On the expected behaviour of noise regularised deep neural networks as Gaussian processes
Arnu Pretorius, Herman Kamper, Steve Kroon
Recent work has established the equivalence between deep neural networks and Gaussian processes (GPs), resulting in so-called neural network Gaussian processes (NNGPs). The behavio…
If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on ReLU networks
Arnu Pretorius, Elan van Biljon, Benjamin van Niekerk +6
Recent work in signal propagation theory has shown that dropout limits the depth to which information can propagate through a neural network. In this paper, we investigate the effe…
Cross-lingual topic prediction for speech using translations
Sameer Bansal, Herman Kamper, Adam Lopez +1
Given a large amount of unannotated speech in a low-resource language, can we classify the speech utterances by topic? We consider this question in the setting where a small amount…
Semantic query-by-example speech search using visual grounding
Herman Kamper, Aristotelis Anastassiou, Karen Livescu
A number of recent studies have started to investigate how speech systems can be trained on untranscribed speech by leveraging accompanying images at training time. Examples of tas…
On the Contributions of Visual and Textual Supervision in Low-Resource Semantic Speech Retrieval
Ankita Pasad, Bowen Shi, Herman Kamper +1
Recent work has shown that speech paired with images can be used to learn semantically meaningful speech representations even without any textual supervision. In real-world low-res…
Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks
Ryan Eloff, André Nortje, Benjamin van Niekerk +7
For our submission to the ZeroSpeech 2019 challenge, we apply discrete latent-variable neural networks to unlabelled speech and use the discovered units for speech synthesis. Unsup…