The **DELU** is a type of activation function that has trainable parameters, uses the complex linear and exponential functions in the positive dimension and uses the **[SiLU](https://paperswithcode.com/method/silu)** in the negative dimension.

$$DELU(x) = SiLU(x), x \leqslant 0$$
$$DELU(x) = (n + 0.5)x + |e^{-x} - 1|, x > 0$$

**CSPResNeXt** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNeXt](https://paperswithcode.com/method/resnext). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.

CSPResNeXt

CSPNet: A New Backbone that can Enhance Learning Capability of CNN

DELU

Trainable Activations for Image Classification

**Domain Adaptive Ensemble Learning**, or **DAEL**, is an architecture for domain adaptation. The model is composed of a CNN feature extractor shared across domains and multiple classifier heads each trained to specialize in a particular source domain. Each such classifier is an expert to its own domain and a non-expert to others. DAEL aims to learn these experts collaboratively so that when forming an ensemble, they can leverage complementary information from each other to be more effective for an unseen target domain. To this end, each source domain is used in turn as a pseudo-target-domain with its own expert providing supervisory signal to the ensemble of non-experts learned from the other sources. For unlabeled target data under the UDA setting where real expert does not exist, DAEL uses pseudo-label to supervise the ensemble learning.

Source	Trainable Activations for Image Classification
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com