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Found 5 papers, 2 papers with code
Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models
We comprehensively evaluate the performance of our model on a variety of noisy inverse problems, including inpainting, denoising, and super-resolution.
SwiFT: Swin 4D fMRI Transformer
To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner.
Continual Learning in the Presence of Spurious Correlation
Specifically, we first show through two-task CL experiments that standard CL methods, which are unaware of dataset bias, can transfer biases from one task to another, both forward and backward, and this transfer is exacerbated depending on whether the CL methods focus on the stability or the plasticity.
Fair Feature Distillation for Visual Recognition
Fairness is becoming an increasingly crucial issue for computer vision, especially in the human-related decision systems.
Uncertainty-based Continual Learning with Adaptive Regularization
We introduce a new neural network-based continual learning algorithm, dubbed as Uncertainty-regularized Continual Learning (UCL), which builds on traditional Bayesian online learning framework with variational inference.
Ranked #11 on
Continual Learning
on ASC (19 tasks)
