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Found 8 papers, 3 papers with code
Dissecting Self-Supervised Learning Methods for Surgical Computer Vision
Correct transfer of these methods to surgery, as described and conducted in this work, leads to substantial performance gains over generic uses of SSL - up to 7. 4% on phase recognition and 20% on tool presence detection - as well as state-of-the-art semi-supervised phase recognition approaches by up to 14%.
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Semantic Segmentation
on Endoscapes
A sampling-based approach for efficient clustering in large datasets
We propose a simple and efficient clustering method for high-dimensional data with a large number of clusters.
ProSper -- A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions
The library widens the scope of dictionary learning approaches beyond implementations of standard approaches such as ICA, NMF or standard L1 sparse coding.
Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications.
Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties
We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory.
Truncated Variational Sampling for "Black Box" Optimization of Generative Models
We investigate the optimization of two probabilistic generative models with binary latent variables using a novel variational EM approach.
Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities
We introduce a solid harmonic wavelet scattering representation, invariant to rigid motion and stable to deformations, for regression and classification of 2D and 3D signals.
What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach
By far most approaches to unsupervised learning learning of visual features, such as sparse coding or ICA, account for translations by representing the same features at different positions.
