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Found 5 papers, 2 papers with code
Do text-free diffusion models learn discriminative visual representations?
We find that the intermediate feature maps of the U-Net are diverse, discriminative feature representations.
Diffusion Models Beat GANs on Image Classification
We explore optimal methods for extracting and using these embeddings for classification tasks, demonstrating promising results on the ImageNet classification task.
A Frequency Perspective of Adversarial Robustness
Our analysis shows that adversarial examples are neither in high-frequency nor in low-frequency components, but are simply dataset dependent.
Weakly Supervised Lesion Co-segmentation on CT Scans
In this work, we propose a weakly-supervised co-segmentation model that first generates pseudo-masks from the RECIST slices and uses these as training labels for an attention-based convolutional neural network capable of segmenting common lesions from a pair of CT scans.
Weakly-Supervised Lesion Segmentation on CT Scans using Co-Segmentation
Lesion segmentation on computed tomography (CT) scans is an important step for precisely monitoring changes in lesion/tumor growth.
