Siamese U-Net model with a pre-trained ResNet34 architecture as an encoder for data efficient Change Detection

**Hierarchical Network Dissection** is a pipeline for interpreting the internal representation of face-centric inference models. Using a probabilistic formulation, Hierarchical Network Dissection pairs units of the model with concepts in a "Face Dictionary" (a collection of facial concepts with corresponding sample images). Interpretable units are discovered in a [convolution](https://paperswithcode.com/method/convolution) layer through HND to identify multiple instances of unit-concept affinity. The pipeline is inspired by [Network Dissection](https://paperswithcode.com/method/network-dissection), an interpretability model for object-centric and scene-centric models.

Hierarchical Network Dissection

Interpreting Face Inference Models using Hierarchical Network Dissection

Siamese U-Net

Deep Active Learning in Remote Sensing for data efficient Change Detection

Unsupervised knowledge distillation from a pretrained language model to *itself*, by alternating between its bi- and cross-encoder forms.

Source	Deep Active Learning in Remote Sensing for data efficient Change Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com