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Found 6 papers, 4 papers with code
D3: Data Diversity Design for Systematic Generalization in Visual Question Answering
We demonstrate that this result is independent of the similarity between the training and testing data and applies to well-known families of neural network architectures for VQA (i. e. monolithic architectures and neural module networks).
Transformer Module Networks for Systematic Generalization in Visual Question Answering
We reveal that Neural Module Networks (NMNs), i. e., question-specific compositions of modules that tackle a sub-task, achieve better or similar systematic generalization performance than the conventional Transformers, even though NMNs' modules are CNN-based.
The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions
Datasets often contain input dimensions that are unnecessary to predict the output label, e. g. background in object recognition, which lead to more trainable parameters.
How Modular Should Neural Module Networks Be for Systematic Generalization?
Neural Module Networks (NMNs) aim at Visual Question Answering (VQA) via composition of modules that tackle a sub-task.
The Foes of Neural Network’s Data Efficiency Among Unnecessary Input Dimensions
In this paper, we investigate the impact of unnecessary input dimensions on one of the central issues of machine learning: the number of training examples needed to achieve high generalization performance, which we refer to as the network's data efficiency.
Frivolous Units: Wider Networks Are Not Really That Wide
We identify two distinct types of "frivolous" units that proliferate when the network's width is increased: prunable units which can be dropped out of the network without significant change to the output and redundant units whose activities can be expressed as a linear combination of others.
