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Found 6 papers, 6 papers with code
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
We show that the idea can be extended to uncertainty quantification: by modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity, and consequently well-calibrated estimates of epistemic uncertainty, with low computational overhead in comparison.
Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision
Visual re-localization means using a single image as input to estimate the camera's location and orientation relative to a pre-recorded environment.
Crop mapping from image time series: deep learning with multi-scale label hierarchies
The three-level label hierarchy is encoded in a convolutional, recurrent neural network (convRNN), such that for each pixel the model predicts three labels at different level of granularity.
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations
We propose to use neural ordinary differential equations (NODEs) in combination with RNNs to classify crop types in irregularly spaced image sequences.
Gating Revisited: Deep Multi-layer RNNs That Can Be Trained
We propose a new STAckable Recurrent cell (STAR) for recurrent neural networks (RNNs), which has fewer parameters than widely used LSTM and GRU while being more robust against vanishing or exploding gradients.
A Layer-Based Sequential Framework for Scene Generation with GANs
The visual world we sense, interpret and interact everyday is a complex composition of interleaved physical entities.
