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Found 5 papers, 3 papers with code
On the Study of Sample Complexity for Polynomial Neural Networks
As a general type of machine learning approach, artificial neural networks have established state-of-art benchmarks in many pattern recognition and data analysis tasks.
Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation
To further mine the non-salient region objects, we propose to exert the segmentation network's self-correction ability.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Jo-SRC: A Contrastive Approach for Combating Noisy Labels
Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance.
Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones
Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators.
Data-driven Meta-set Based Fine-Grained Visual Classification
To this end, we propose a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition.
