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Found 10 papers, 8 papers with code
CRCE: Coreference-Retention Concept Erasure in Text-to-Image Diffusion Models
Text-to-Image diffusion models can produce undesirable content that necessitates concept erasure.
BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuning
To mitigate these challenges, we propose Bias-based Weight Masking Fine-Tuning (BMFT), a novel post-processing method that enhances the fairness of a trained model in significantly fewer epochs without requiring access to the original training data.
ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolution
Compared to the more complicated full fine-tuning approach, our method reduces training time and memory consumption by 57% and 15%, respectively.
parameter-efficient fine-tuning
Stereo Image Super-Resolution
Erase to Enhance: Data-Efficient Machine Unlearning in MRI Reconstruction
This paper shows that machine unlearning is possible in MRI tasks and has the potential to benefit for bias removal.
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
Inference Stage Denoising for Undersampled MRI Reconstruction
In this work, by employing a conditional hyperparameter network, we eliminate the need of augmentation, yet maintain robust performance under various levels of Gaussian noise.
Toward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image Information
Real-world stereo image super-resolution has a significant influence on enhancing the performance of computer vision systems.
Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction
Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time.
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement
Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's function and condition in a non-invasive manner.
Attention Based Image Compression Post-Processing Convolutional Neural Network
The traditional image compressors, e. g., BPG and H. 266, have achieved great image and video compression quality.
