Existing deep learning based solutions typically restore the target layers individually, or with some concerns at the end of the output, barely taking into account the interaction across thetwostreams/branches. Inorder toutilize information more efficiently, this work presents a general yet simple interactive strategy, namely your trash is my treasure(YTMT), for constructing dual-stream decomposition networks.

Noisy labels pose a common challenge for training accurate deep neural networks. To mitigate label noise, prior studies have proposed various robust loss functions to achieve noise tolerance in the presence of label noise, particularly symmetric losses.

The packages developed as part of this work are discussed below and made available via the above notebooks. This simply calls the code fromhttps://github.com/charliemarx/ Figure 1 shows the the diagnostic graphs as considered in [4]. Note that the notebook does not haveafixedseed and this instability can beexplored by re-runningthenotebook. SHAP values are calculated on an identical RandomForestClassifier as used for the RF MCR. Thegraphs generated bytheNotebooks areperMCR estimation method, rather thanthe comparison graphs shown in the paper.