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Found 5 papers, 2 papers with code
Contextualising Implicit Representations for Semantic Tasks
Prior works have demonstrated that implicit representations trained only for reconstruction tasks typically generate encodings that are not useful for semantic tasks.
Approximating Continuous Convolutions for Deep Network Compression
We present ApproxConv, a novel method for compressing the layers of a convolutional neural network.
Towards Generalising Neural Implicit Representations
Neural implicit representations have shown substantial improvements in efficiently storing 3D data, when compared to conventional formats.
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes
We propose a novel method for neural network quantization that casts the neural architecture search problem as one of hyperparameter search to find non-uniform bit distributions throughout the layers of a CNN.
Correspondence Networks with Adaptive Neighbourhood Consensus
This is a challenging task due to large intra-class variations and a lack of dense pixel level annotations.
Ranked #11 on
Semantic correspondence
on PF-PASCAL
