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Found 7 papers, 5 papers with code
Reuse out-of-year data to enhance land cover mappingvia feature disentanglement and contrastive learning
Typically, when creating a land cover (LC) map, precise ground truth data is collected through time-consuming and expensive field campaigns.
PDiscoNet: Semantically consistent part discovery for fine-grained recognition
Fine-grained classification often requires recognizing specific object parts, such as beak shape and wing patterns for birds.
Masking Strategies for Background Bias Removal in Computer Vision Models
Models for fine-grained image classification tasks, where the difference between some classes can be extremely subtle and the number of samples per class tends to be low, are particularly prone to picking up background-related biases and demand robust methods to handle potential examples with out-of-distribution (OOD) backgrounds.
Towards Explainable Land Cover Mapping: a Counterfactual-based Strategy
Furthermore, plausibility/realism of the generated counterfactual explanations is enforced via the proposed adversarial learning strategy.
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.
Accelerating Non-Negative and Bounded-Variable Linear Regression Algorithms with Safe Screening
Non-negative and bounded-variable linear regression problems arise in a variety of applications in machine learning and signal processing.
Expanding boundaries of Gap Safe screening
In this work, we extend the existing Gap Safe screening framework by relaxing the global strong-concavity assumption on the dual cost function.
