**PeleeNet** is a convolutional neural network  and object detection backbone that is a variation of [DenseNet](https://paperswithcode.com/method/densenet) with optimizations to meet a memory and computational budget. Unlike competing networks, it does not use depthwise convolutions and instead relies on regular convolutions.

ALP-GMM is is an algorithm that learns to generate a learning curriculum for black box reinforcement learning agents, whereby it sequentially samples parameters controlling a stochastic procedural generation of tasks or environments.

ALP-GMM

Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments

PeleeNet

Pelee: A Real-Time Object Detection System on Mobile Devices

A **Dual Path Network** block is an image model block used in convolutional neural network. The idea of this module is to enable sharing of common features while maintaining the flexibility to explore new features through dual path architectures. In this sense it combines the benefits of [ResNets](https://paperswithcode.com/method/resnet) and [DenseNets](https://paperswithcode.com/method/densenet). It was proposed as part of the [DPN](https://paperswithcode.com/method/dpn) CNN architecture.

We formulate such a dual path architecture as follows:

$$x^{k} = \sum\limits\_{t=1}^{k-1} f\_t^{k}(h^t) \text{,}  $$

$$
y^{k} = \sum\limits\_{t=1}^{k-1} v\_t(h^t) = y^{k-1} + \phi^{k-1}(y^{k-1}) \text{,} \\\\
$$

$$
r^{k} = x^{k} + y^{k} \text{,} \\\\
$$

$$
h^k = g^k \left( r^{k} \right) \text{,}
$$

where $x^{k}$ and $y^{k}$ denote the extracted information at $k$-th step from individual path, $v_t(\cdot)$ is a feature learning function as $f_t^k(\cdot)$. The first equation refers to the densely connected path that enables exploring new features. The second equation refers to the residual path that enables common features re-usage. The third equation defines the dual path that integrates them and feeds them to the last transformation function in the last equation.

Source	Pelee: A Real-Time Object Detection System on Mobile Devices
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com