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Found 7 papers, 3 papers with code
Prompt Tuning for Parameter-efficient Medical Image Segmentation
As such, fine-tuning a model to a downstream task in a parameter-efficient but effective way, e. g. for a new set of classes in the case of semantic segmentation, is of increasing importance.
TTAPS: Test-Time Adaption by Aligning Prototypes using Self-Supervision
Nowadays, deep neural networks outperform humans in many tasks.
Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows
One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation
To avoid the costly annotation of training data for unseen domains, unsupervised domain adaptation (UDA) attempts to provide efficient knowledge transfer from a labeled source domain to an unlabeled target domain.
Ranked #20 on
Synthetic-to-Real Translation
on SYNTHIA-to-Cityscapes
MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption
By minimizing the self-supervised loss, we learn task-specific model parameters for different tasks.
Anomaly Detection Based on Selection and Weighting in Latent Space
Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD.
Training Variational Auto Encoders with Discrete Latent Representations using Importance Sampling
In this paper, we propose an easy method to train VAEs with binary or categorically valued latent representations.
