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Page 158                            Ji et al. Intell Robot 2021;1(2):151-75  https://dx.doi.org/10.20517/ir.2021.14


































                                                Figure 3. A sample U-Net architecture.





























                                                    Figure 4. Transfer learning.

               anomalies and the location of the anomalies need to be identified. It means classification tasks and
               localization tasks need to be performed concurrently, which is semantic segmentation. Classification
               networks are created to be invariant to translation and rotation, thus giving no importance to location
               information, whereas localization involves getting accurate details with respect to the location. Thus, these
               two tasks are inherently contradictory. Most segmentation algorithms give more importance to localization
               and thus lose sight of the global context.
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