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Found 9 papers, 4 papers with code
Towards Generalist Biomedical AI
While considerable work is needed to validate these models in real-world use cases, our results represent a milestone towards the development of generalist biomedical AI systems.
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild
Diffusion models have shown promising results on single-image super-resolution and other image- to-image translation tasks.
Scalable Adaptive Computation for Iterative Generation
We show how to leverage recurrence by conditioning the latent tokens at each forward pass of the reverse diffusion process with those from prior computation, i. e. latent self-conditioning.
Ranked #1 on
Image Generation
on ImageNet 64x64
Robust and Efficient Medical Imaging with Self-Supervision
These results suggest that REMEDIS can significantly accelerate the life-cycle of medical imaging AI development thereby presenting an important step forward for medical imaging AI to deliver broad impact.
Disentangling Architecture and Training for Optical Flow
Our newly trained RAFT achieves an Fl-all score of 4. 31% on KITTI 2015, more accurate than all published optical flow methods at the time of writing.
Differentiable probabilistic models of scientific imaging with the Fourier slice theorem
Finally, we demonstrate how the reconstruction algorithm can be extended with an amortized inference scheme on unknown attributes such as object pose.
TzK: Flow-Based Conditional Generative Model
We formulate a new class of conditional generative models based on probability flows.
On the effectiveness of task granularity for transfer learning
We describe a DNN for video classification and captioning, trained end-to-end, with shared features, to solve tasks at different levels of granularity, exploring the link between granularity in a source task and the quality of learned features for transfer learning.
Subspace Selection to Suppress Confounding Source Domain Information in AAM Transfer Learning
We propose a subspace transfer learning method, in which we select a subspace from the source that best describes the target space.
