**DistanceNet** is a learning algorithm for multi-source domain adaptation that uses various distance measures, or a mixture of these distance measures, as an additional loss function to be minimized jointly with the task's loss function, so as to achieve better unsupervised domain adaptation.

**Restricted Boltzmann Machines**, or **RBMs**, are two-layer generative neural networks that learn a probability distribution over the inputs. They are a special class of Boltzmann Machine in that they have a restricted number of connections between visible and hidden units. Every node in the visible layer is connected to every node in the hidden layer, but no nodes in the same group are connected. RBMs are usually trained using the contrastive divergence learning procedure.

Image Source: [here](https://medium.com/datatype/restricted-boltzmann-machine-a-complete-analysis-part-1-introduction-model-formulation-1a4404873b3)

Restricted Boltzmann Machine

DistanceNet

Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits

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.

Source	Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com