**AutoSync** is a pipeline for automatically optimizing synchronization strategies, given model structures and resource specifications, in data-parallel distributed machine learning. By factorizing the synchronization strategy with respect to each trainable building block of a DL model, we can construct a valid and large strategy space spanned by multiple factors. AutoSync efficiently navigates the space and locates the optimal strategy. AutoSync leverages domain knowledge about synchronization systems to reduce the search space, and is equipped with a domain adaptive simulator, which combines principled communication modeling and data-driven ML models, to estimate the runtime of strategy proposals without launching real distributed execution.

**IFBlock** is a video model block used in the [IFNet](https://paperswithcode.com/method/ifnet) architecture for video frame interpolation. IFBlocks do not contain expensive operators like cost volume or forward warping and use 3 × 3 convolution and deconvolution as building blocks. Each IFBlock has a feed-forward structure consisting of several convolutional layers and an upsampling operator. Except for the layer that outputs the optical flow residuals and the fusion map, [PReLU](https://paperswithcode.com/method/prelu) activations are used.

IFBlock

RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

AutoSync

AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning

**DropAttack** is an adversarial training method that adds intentionally worst-case adversarial perturbations to both the input and hidden layers in different dimensions and minimizes the adversarial risks generated by each layer.

Source	AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com