using transfer learning to transfer knowledge from one building to predict the energy consumption of another building with scarce data

**ShapeConv**, or **Shape-aware Convolutional layer**, is a convolutional layer for processing the depth feature in indoor RGB-D semantic segmentation. The depth feature is firstly decomposed into a shape-component and a base-component, next two learnable weights are introduced to cooperate with them independently, and finally a [convolution](https://paperswithcode.com/method/convolution) is applied on the re-weighted combination of these two components.

ShapeConv

ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation

b2b transfer learning

Transfer Learning in Deep Learning Models for Building Load Forecasting: Case of Limited Data

**HardELiSH** is an activation function for neural networks.  The HardELiSH is a multiplication of the [HardSigmoid](https://paperswithcode.com/method/hard-sigmoid) and [ELU](https://paperswithcode.com/method/elu) in the negative part and a multiplication of the Linear and the HardSigmoid in the positive
part:

$$f\left(x\right) = x\max\left(0, \min\left(1, \left(\frac{x+1}{2}\right)\right) \right) \text{ if } x \geq 1$$
$$f\left(x\right) = \left(e^{x}-1\right)\max\left(0, \min\left(1, \left(\frac{x+1}{2}\right)\right)\right) \text{ if } x < 0 $$

Source: [Activation Functions](https://arxiv.org/pdf/1811.03378.pdf)

Source	Transfer Learning in Deep Learning Models for Building Load Forecasting: Case of Limited Data
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com