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Found 9 papers, 5 papers with code
Interpreting the Weight Space of Customized Diffusion Models
First, as each point in the space corresponds to an identity, sampling a set of weights from it results in a model encoding a novel identity.
Idempotent Generative Network
We define the target manifold as the set of all instances that $f$ maps to themselves.
Rosetta Neurons: Mining the Common Units in a Model Zoo
In this paper, we demonstrate the existence of common features we call "Rosetta Neurons" across a range of models with different architectures, different tasks (generative and discriminative), and different types of supervision (class-supervised, text-supervised, self-supervised).
DeepCOVID-Fuse: A Multi-modality Deep Learning Model Fusing Chest X-Radiographs and Clinical Variables to Predict COVID-19 Risk Levels
The pre-trained fusion model with only CXRs as input increases accuracy to 0. 632 and AUC to 0. 813 and with only clinical variables as input increases accuracy to 0. 539 and AUC to 0. 733.
BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos
In this way, we discover keypoints without requiring manual supervision in videos of humans and rats, demonstrating the potential of 3D keypoint discovery for studying behavior.
medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space
Despite the surge of deep learning in the past decade, some users are skeptical to deploy these models in practice due to their black-box nature.
Investigating the Potential of Auxiliary-Classifier GANs for Image Classification in Low Data Regimes
Generative Adversarial Networks (GANs) have shown promise in augmenting datasets and boosting convolutional neural networks' (CNN) performance on image classification tasks.
Visual Explanations for Convolutional Neural Networks via Latent Traversal of Generative Adversarial Networks
Lack of explainability in artificial intelligence, specifically deep neural networks, remains a bottleneck for implementing models in practice.
Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes
We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures.
