Link to Arxiv (DeepLabv1): https://arxiv.org/abs/1606.00915 https://arxiv.org/abs/1606.00915 Link to Arxiv (DeepLabv3): https://arxiv.org/abs/1706.05587 https:

Link to Arxiv (DeepLabv1): https://arxiv.org/abs/1606.00915 https://arxiv.org/abs/1606.00915 Link to Arxiv (DeepLabv3): https://arxiv.org/abs/1706.05587 https://arxiv.org/abs/1706.05587 Link to GitHub: https://github.com/tensorflow/models/tree/master/research/deeplab https://github.com/tensorflow/models/tree/master/research/de... The README on there has a very neat TLDR of the model: "DeepLabv1 [1]: We use atrous convolution ['s a shorthand for convolution with upsampled filter'] to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. DeepLabv2 [2]: We use atrous spatial pyramid pooling (ASPP) ['a computationally efficient scheme of resampling a given feature layer at multiple rates prior to convolution'] to robustly segment objects at multiple scales with filters at multiple sampling rates and effective fields-of-views. DeepLabv3 [3]: We augment the ASPP module with image-level feature [5, 6] to capture longer range information. We also include batch normalization [7] parameters to facilitate the training. In particular, we applying atrous convolution to extract output features at different output strides during training and evaluation, which efficiently enables training BN at output stride = 16 and attains a high performance at output stride = 8 during evaluation. DeepLabv3+ [4]: We extend DeepLabv3 to include a simple yet effective decoder module to refine the segmentation results especially along object boundaries. Furthermore, in this encoder-decoder structure one can arbitrarily control the resolution of extracted encoder features by atrous convolution to trade-off precision and runtime."