**SimAug**, or **Simulation as Augmentation**, is a data augmentation method for trajectory prediction. It augments the representation such that it is robust to the variances in semantic scenes and camera views.  First, to deal with the gap between real and synthetic semantic scene, it represents each training trajectory by high-level scene semantic segmentation features, and defends the model from adversarial examples generated by whitebox attack methods. Second, to overcome the changes in camera views, it generates multiple views for the same trajectory, and encourages the model to focus on the “hardest” view to which the model has learned. The classification loss is adopted and the view with the highest loss is favored during training. Finally, the augmented trajectory is computed as a convex combination of the trajectories generated in previous steps. The trajectory prediction model is built on a multi-scale representation and the final model is trained to minimize the empirical vicinal risk over the distribution of augmented trajectories.

Recently, [dense connections](https://paperswithcode.com/method/dense-connections) have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, [DenseNet](https://paperswithcode.com/method/densenet) that connects each layer to every other layer in a feed-forward fashion and has shown impressive performances in natural image classification tasks. We propose HyperDenseNet, a 3-D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems. Each imaging modality has a path, and dense connections occur not only between the pairs of layers within the same path but also between those across different paths. This contrasts with the existing multi-modal CNN approaches, in which modeling several modalities relies entirely on a single joint layer (or level of abstraction) for fusion, typically either at the input or at the output of the network. Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation. We report extensive evaluations over two different and highly competitive multi-modal brain tissue segmentation challenges, iSEG 2017 and MRBrainS 2013, with the former focusing on six month infant data and the latter on adult images. HyperDenseNet yielded significant improvements over many state-of-the-art segmentation networks, ranking at the top on both benchmarks. We further provide a comprehensive experimental analysis of features re-use, which confirms the importance of hyper-dense connections in multi-modal representation learning.

HyperDenseNet

HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation

SimAug

SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen Cameras

**1-bit LAMB** is a communication-efficient stochastic optimization technique which introduces a novel way to support adaptive layerwise learning rates even when communication is compressed. Learning from the insights behind [1-bit Adam](https://paperswithcode.com/method/1-bit-adam), it is a a 2-stage algorithm which uses [LAMB](https://paperswithcode.com/method/lamb) (warmup stage) to “pre-condition” a communication compressed momentum SGD algorithm (compression stage). At compression stage where original LAMB algorithm cannot be used to update the layerwise learning rates, 1-bit LAMB employs a novel way to adaptively scale layerwise learning rates based on information from both warmup and compression stages. As a result, 1-bit LAMB is able to achieve large batch optimization (LAMB)’s convergence speed under compressed communication.

There are two major differences between 1-bit LAMB and the original LAMB:

- During compression stage, 1-bit LAMB updates the layerwise learning rate based on a novel “reconstructed gradient” based on the compressed momentum. This makes 1-bit LAMB compatible with error compensation and be able to keep track of the training dynamic under compression.
- 1-bit LAMB also introduces extra stabilized soft thresholds when updating layerwise learning rate at compression stage, which makes training more stable under compression.

Source	SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen Cameras
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com