Reflective-Net: Learning from Explanations
Humans possess a remarkable capability to make fast, intuitive decisions, but also to self-reflect, i.e., to explain to oneself, and to efficiently learn from explanations by others. This work provides the first steps toward mimicking this process by capitalizing on the explanations generated based on existing explanation methods, i.e. Grad-CAM. Learning from explanations combined with conventional labeled data yields significant improvements for classification in terms of accuracy and training time.
PDF AbstractTasks
Datasets
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here