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Unsupervised Accuracy Estimation of Deep Visual Models using Domain-Adaptive Adversarial Perturbation without Source Samples
Deploying deep visual models can lead to performance drops due to the discrepancies between source and target distributions.
Self-Supervised Contrastive Representation Learning for 3D Mesh Segmentation
3D deep learning is a growing field of interest due to the vast amount of information stored in 3D formats.
PatchNet: Unsupervised Object Discovery based on Patch Embedding
Key to this approach is the pattern space, a latent space of patterns that represents all possible sub-images of the given image data.
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification
We propose a highly data-efficient active learning framework for image classification.
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