**Deflation** is a video-to-image operation to transform a video network into a network that can ingest a single image. In the two types of video networks considered in the original paper, this deflation corresponds to the following operations: for [3D convolutional based networks](https://paperswithcode.com/method/3d-convolution), summing the 3D spatio-temporal filters over the temporal dimension to obtain 2D filters; for TSM networks,, turning off the channel shifting which results in a standard [residual architecture](https://paperswithcode.com/method/resnet) (ResNet50) for images.

**Canvas Method** is a method for inference attacks on object detection models. It draws a predicted bounding box distribution on an empty canvas for an attack model input. The canvas is initially set to an image of 300$\times$300 pixels in size, where every pixel has a value of zero and the boxes drawn on the canvas have the same center as the predicted boxes and the same intensity as the prediction scores.

Canvas Method

Membership Inference Attacks Against Object Detection Models

Deflation

Self-Supervised MultiModal Versatile Networks

**FORK**, or **Forward Looking Actor** is a type of actor for actor-critic algorithms. In particular, FORK includes a neural network that forecasts the next state given the current state and current action, called system network; and a neural network that forecasts the
reward given a (state, action) pair, called reward network. With the system network and reward network, FORK can forecast the next state and consider the value of the next state when improving the policy.

Source	Self-Supervised MultiModal Versatile Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com