Code for NeurIPS 2023 paper - FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning

An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730

A Python library for signal decomposition algorithms

FITS: Frequency Interpolation Time Series Analysis Baseline

[ICML 2024] A novel, efficient lightweight approach combining convolutional operations with adaptive spectral analysis as a foundation model for different time series tasks

A comparative study on Self-Supervised Learning for Time Series: Contrastive or Generative?

A universal time series representation learning framework

A curated list of state-of-the-art papers on deep learning for universal representations of time series.

A unified multi-task time series model.

A Library for Advanced Deep Time Series Models.

Tools for generating mini-ImageNet dataset and processing batches

[ICLR 2024] - Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental Learning

Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.

🎉 PILOT: A Pre-trained Model-Based Continual Learning Toolbox

LibFewShot: A Comprehensive Library for Few-shot Learning. TPAMI 2023.

[ICCV 2023] Prototype Reminiscence and Augmented Asymmetric Knowledge Aggregation for Non-Exemplar Class-Incremental Learning

Awesome Incremental Learning

This repository will be posting analytic continual learning series, including Analytic Class-Incremental Learning (ACIL), Gaussian Kernel Embedded Analytic Learning (GKEAL), Dual-Stream Analytic Le…

A list of papers that studies Novel Class Discovery

PyCIL: A Python Toolbox for Class-Incremental Learning

Benchmarking Generalized Out-of-Distribution Detection

Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning

[CVPR 2023] Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning

PyTorch Implementation of the CVPR'22 Paper "Constrained Few-shot Class-incremental Learning"

Python code for "Probabilistic Machine learning" book by Kevin Murphy

Implementation of Open-Set Likelihood Maximization for Few-Shot Learning