**Memory-Associated Differential** (**MAD**) Learning was developed to inference from the memorized facts that we already know to predict what we want to know.

Image source: [Luo et al.](https://arxiv.org/pdf/2102.05246v1.pdf)

With the increasing demand of autonomous systems, pixelwise semantic segmentation for visual scene understanding needs to be not only accurate but also efficient for potential real-time applications. In this paper, we propose Context Aggregation Network, a dual branch convolutional neural network, with significantly lower computational costs as compared to the state-of-the-art, while maintaining a competitive prediction accuracy. Building upon the existing dual branch architectures for high-speed semantic segmentation, we design a high resolution branch for effective spatial detailing and a context branch with light-weight versions of global aggregation and local distribution blocks, potent to capture both long-range and local contextual dependencies required for accurate semantic segmentation, with low computational overheads. We evaluate our method on two semantic segmentation datasets, namely Cityscapes dataset and UAVid dataset. For Cityscapes test set, our model achieves state-of-the-art results with mIOU of 75.9%, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. With regards to UAVid dataset, our proposed network achieves mIOU score of 63.5% with high execution speed (15 FPS).

CABiNet

MAD Learning

Memory-Associated Differential Learning

**TD-VAE**, or **Temporal Difference VAE**, is a generative sequence model that learns representations containing explicit beliefs about states several steps into the future, and that can be rolled out directly without single-step transitions. TD-VAE is trained on pairs of temporally separated time points, using an analogue of [temporal difference learning](https://paperswithcode.com/method/td-lambda) used in reinforcement learning.

Source	Memory-Associated Differential Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com