A **Masked Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) which masks certain pixels so that the model can only predict based on pixels already seen. This type of convolution was introduced with [PixelRNN](https://paperswithcode.com/method/pixelrnn) generative models, where an image is generated pixel by pixel, to ensure that the model was conditional only on pixels already visited.

A **BiFPN**, or **Weighted Bi-directional Feature Pyramid Network**, is a type of feature pyramid network which allows easy and fast multi-scale feature fusion. It incorporates the multi-level feature fusion idea from [FPN](https://paperswithcode.com/method/fpn), [PANet](https://paperswithcode.com/method/panet) and [NAS-FPN](https://paperswithcode.com/method/nas-fpn) that enables information to flow in both the top-down and bottom-up directions, while using regular and efficient connections. It also utilizes a fast normalized fusion technique. Traditional approaches usually treat all features input to the FPN equally, even those with different resolutions. However, input features at different resolutions often have unequal contributions to the output features. Thus, the BiFPN adds an additional weight for each input feature allowing the network to learn the importance of each. All regular convolutions are also replaced with less expensive depthwise separable convolutions.

Comparing with PANet, PANet added an extra bottom-up path for information flow at the expense of more computational cost. Whereas BiFPN optimizes these cross-scale connections by removing nodes with a single input edge, adding an extra edge from the original input to output node if they are on the same level, and treating each bidirectional path as one feature network layer (repeating it several times for more high-level future fusion).

BiFPN

EfficientDet: Scalable and Efficient Object Detection

Masked Convolution

Pixel Recurrent Neural Networks

The principle of parsimony, also known as Occam's razor, elucidates the preference for the simplest explanation that provides optimal results when faced with multiple options. Thus, we can assert that the principle of parsimony is justified by "the assumption that is both the simplest and contains all the necessary information required to comprehend the experiment at hand." This principle finds application in various scenarios or events in our daily lives, including predictions in Data Science models.

It is widely recognized that a less complex model will produce more stable predictions, exhibit greater resilience to noise and disturbances, and be more manageable for maintenance and analysis. Additionally, reducing the number of features can lead to further cost savings by diminishing the use of sensors, lowering energy consumption, minimizing information acquisition costs, reducing maintenance requirements, and mitigating the necessity to retrain models due to feature fluctuations caused by noise, outliers, data drift, etc.

The concurrent optimization of hyperparameters (HO) and feature selection (FS) for achieving Parsimonious Model Selection (PMS) is an ongoing area of active research. Nonetheless, the effective selection of appropriate hyperparameters and feature subsets presents a challenging combinatorial problem, frequently requiring the application of efficient heuristic methods.

Source	Pixel Recurrent Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com