**VoVNetV2** is a convolutional neural network that improves upon [VoVNet](https://paperswithcode.com/method/vovnet) with two effective strategies: (1) [residual connection](https://paperswithcode.com/method/residual-connection) for alleviating the optimization problem of larger VoVNets and (2) effective Squeeze-Excitation (eSE) dealing with the channel information loss problem of the original squeeze-and-excitation module.

**MAML**, or **Model-Agnostic Meta-Learning**, is a model and task-agnostic algorithm for meta-learning that trains a model’s parameters such that a small number of gradient updates will lead to fast learning on a new task.

Consider a model represented by a parametrized function $f\_{\theta}$ with parameters $\theta$. When adapting to a new task $\mathcal{T}\_{i}$, the model’s parameters $\theta$ become $\theta'\_{i}$. With MAML, the updated parameter vector $\theta'\_{i}$ is computed using one or more gradient descent updates on task $\mathcal{T}\_{i}$. For example, when using one gradient update,

$$ \theta'\_{i} = \theta - \alpha\nabla\_{\theta}\mathcal{L}\_{\mathcal{T}\_{i}}\left(f\_{\theta}\right) $$

The step size $\alpha$ may be fixed as a hyperparameter or metalearned. The model parameters are trained by optimizing for the performance of $f\_{\theta'\_{i}}$ with respect to $\theta$ across tasks sampled from $p\left(\mathcal{T}\_{i}\right)$. More concretely the meta-objective is as follows:

$$ \min\_{\theta} \sum\_{\mathcal{T}\_{i} \sim p\left(\mathcal{T}\right)} \mathcal{L}\_{\mathcal{T\_{i}}}\left(f\_{\theta'\_{i}}\right) = \sum\_{\mathcal{T}\_{i} \sim p\left(\mathcal{T}\right)} \mathcal{L}\_{\mathcal{T\_{i}}}\left(f\_{\theta - \alpha\nabla\_{\theta}\mathcal{L}\_{\mathcal{T}\_{i}}\left(f\_{\theta}\right)}\right) $$

Note that the meta-optimization is performed over the model parameters $\theta$, whereas the objective is computed using the updated model parameters $\theta'$. In effect MAML aims to optimize the model parameters such that one or a small number of gradient steps on a new task will produce maximally effective behavior on that task. The meta-optimization across tasks is performed via stochastic gradient descent ([SGD](https://paperswithcode.com/method/sgd)), such that the model parameters $\theta$ are updated as follows:

$$ \theta \leftarrow \theta - \beta\nabla\_{\theta} \sum\_{\mathcal{T}\_{i} \sim p\left(\mathcal{T}\right)} \mathcal{L}\_{\mathcal{T\_{i}}}\left(f\_{\theta'\_{i}}\right)$$

where $\beta$ is the meta step size.

MAML

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

VoVNetV2

CenterMask : Real-Time Anchor-Free Instance Segmentation

A **Harmonic Network**, or **Harm-Net**, is a type of convolutional neural network that replaces convolutional layers with "harmonic blocks" that use [Discrete Cosine Transform](https://paperswithcode.com/method/discrete-cosine-transform) (DCT) filters. These blocks can be useful in  truncating high-frequency information (possible due to the redundancies in the spectral domain).

Source	CenterMask : Real-Time Anchor-Free Instance Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com