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Found 5 papers, 1 papers with code
Pinpoint Counterfactuals: Reducing social bias in foundation models via localized counterfactual generation
When applied to the Conceptual Captions dataset for creating gender counterfactuals, our method results in higher visual and semantic fidelity than state-of-the-art alternatives, while maintaining the performance of models trained using only real data on non-human-centric tasks.
Improved transferability of self-supervised learning models through batch normalization finetuning
At a cost of extra training of only 0. 16% model parameters, in case of ResNet-50, we acquire a proxy task that (i) has a stronger correlation with end-to-end finetuned performance, (ii) improves the linear probing performance in the many- and few-shot learning regimes and (iii) in some cases, outperforms both linear probing and end-to-end finetuning, reaching the state-of-the-art performance on a pathology dataset.
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Classification
on MHIST
(using extra training data)
Self-Supervised Curricular Deep Learning for Chest X-Ray Image Classification
Deep learning technologies have already demonstrated a high potential to build diagnosis support systems from medical imaging data, such as Chest X-Ray images.
A study on the distribution of social biases in self-supervised learning visual models
We show that there is a correlation between the type of the SSL model and the number of biases that it incorporates.
Improved skin lesion recognition by a Self-Supervised Curricular Deep Learning approach
State-of-the-art deep learning approaches for skin lesion recognition often require pretraining on larger and more varied datasets, to overcome the generalization limitations derived from the reduced size of the skin lesion imaging datasets.
