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Found 5 papers, 4 papers with code
Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation
Furthermore, we propose random cropping as a stochastic aggregation technique that improves the performance of saliency, making it a strong alternative to CAM for WS3.
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)
We also theoretically show that the learning objective of DAM is directly related to minimizing the L0 norm of the masking layer.
PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics
In scientific applications, it is also important to inform the learning of DL models with knowledge of physics of the problem to produce physically consistent and generalized solutions.
Beyond Observed Connections : Link Injection
In this paper, we proposed the \textit{link injection}, a novel method that helps any differentiable graph machine learning models to go beyond observed connections from the input data in an end-to-end learning fashion.
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling Approach
Existing algorithms for this task rely on negative sampling objectives that maximize the similarity in node embeddings at nearby nodes (referred to as "cohesion") by maintaining positive and negative corpus of node pairs.
