NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data

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Code Repo for "Subspace Inference for Bayesian Deep Learning"

[CVPR 2024] The code repository for "Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning" in PyTorch.

The official PyTorch implementation of the paper "Motion Basis Learning for Unsupervised Deep Homography Estimation with Subspace Projection".

LSM: Learning Subspace Minimization for Low-Level Vision (CVPR'2020)

A collection of computationally efficient algorithms for online subspace learning and principal component analysis

Demo implementation of Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition

Code accompanying the ICML-2018 paper "Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace"

Continual Learning in Low-rank Orthogonal Subspaces (NeurIPS'20)

IMTSL - Incremental and Multi-feature Tensor Subspace Learning

A backdoor defense for federated learning via isolated subspace training (NeurIPS2023)

(SIGKDD 2022) Connected Low-Loss Subspace Learning for a Personalization in Federated Learning (https://arxiv.org/abs/2109.07628)

MATLAB implementation for our AAAI-20 paper: Quanxue Gao, Wei Xia, Zhizhen Wan, Deyan Xie, Pu Zhang. Tensor-SVD Based Graph Learning for Multi-View Subspace Clustering. AAAI 2020.

The Official PyTorch implementation of Shared LoRA Subspaces for almost Strict Continual Learning

[CVPR 2025] Self-Learning Hyperspectral and Multispectral Image Fusion via Adaptive Residual Guided Subspace Diffusion Model

[TGRS 2023] Official implementation for 'Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification'

Pytorch UNOFFICIAL implement "NBNet: Noise Basis Learning for Image Denoising with Subspace Projection"

This is the official code for Any-SSR "Analytic Subspace Routing: How Recursive Least Squares Works in Continual Learning of Large Language Model"

Subspace clustering through the deep learning approach

Implementation of "Learning latent subspaces in variational autoencoders"

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ReImplement of 'NBNet: Noise Basis Learning for Image Denoising with Subspace Projection' in Pytorch

Subspace Representation Learning for Sparse Linear Arrays to Localize More Sources than Sensors: A Deep Learning Methodology

It is intended to detect damage to road images taken by a camera. For this, deep learning technology, a subspace of machine learning, and Convolutional Neural Networks (CNN), one of the most popular types of deep neural networks, are used. The TensorFlow library is trained through the Ssd Inception V2 Coco pre-trained model to detect damage to images. As a result of the tests and trainings, the closest determinations are 86%. In order to increase the accuracy of the training, the use of GPU, the magnification of the data set and the number of iterations were considered.

The release code of paper "Multi-view Subspace Clustering via Co-training Robust Data Representation" in IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

Implementation of paper<Joint Feature Selection and Subspace Learning for Cross-Modal Retrieval>

Robust Orthonormal Subspace Learning in Python

Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?