143 Open source projects that are alternatives of or similar to semi-supervised-NFs

Generalized Product Quantization Network For Semi-supervised Image Retrieval - CVPR 2020

Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in full in this ICML 2021 paper: https://arxiv.org/abs/2102.08622.

Tensorflow Implementation on Paper [ECCV2018]Semi-Supervised Deep Learning with Memory

Implementing Billion-scale semi-supervised learning for image classification using Pytorch

PyTorch implementation of the paper "NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity." (NeurIPS 2020)

PyTorch implementation of NVIDIA WaveGlow with constant memory cost.

Code for the paper: "SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification"

Joint Semi-supervised Learning of Image Registration and Segmentation

Complete download for papers in various top conferences

TensorFlow implementation for "Guided Optical Flow Learning"

Continuous-time gradient flow for generative modeling and variational inference

[CVPR 2022] ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation

Code for Paper ''Dual Student: Breaking the Limits of the Teacher in Semi-Supervised Learning'' [ICCV 2019]

supervised and semi-supervised image classification with self-supervision (Keras)

NeuroAI-UW seminar, a regular weekly seminar for the UW community, organized by NeuroAI Shlizerman Lab.

Official code for UnICORNN (ICML 2021)

A Julia framework for invertible neural networks

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

Semi-Supervised Learning and Collective Classification

Keras implementation of temporal ensembling(semi-supervised learning)

Semisupervided LDA for theory-driven text analysis

FedScale is a scalable and extensible open-source federated learning (FL) platform.

Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)

PyTorch Implementation of ByteDance's Cross-speaker Emotion Transfer Based on Speaker Condition Layer Normalization and Semi-Supervised Training in Text-To-Speech

Papers, authors and author affiliations from ICML, NeurIPS and ICLR 2006-2021

Implementation of "Intensity-Free Learning of Temporal Point Processes" (Spotlight @ ICLR 2020)

A Python Library for Deep Probabilistic Modeling

Source code and data for ACL 2019 Long Paper ``Semantic Parsing with Dual Learning".

Pytorch Implementation of HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning (https://arxiv.org/abs/1807.11407)

PyProphet: Semi-supervised learning and scoring of OpenSWATH results.

Invariant representation learning from imaging and spectral data

Source code of the KDD19 paper "Deep anomaly detection with deviation networks", weakly/partially supervised anomaly detection, few-shot anomaly detection

PyTorch implementation of EMAN for self-supervised and semi-supervised learning: https://arxiv.org/abs/2101.08482

Code for ICML 2019 paper "Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering" [long-oral]

Pytorch Implementation of "Adversarial Learning For Semi-Supervised Semantic Segmentation" for ICLR 2018 Reproducibility Challenge

JCLAL is a general purpose framework developed in Java for Active Learning.

Implementation of ICML'2021:Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces

Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology. (DEPRECATED)

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight)

🧹 Formerly for binary classification with noisy labels. Replaced by cleanlab.

EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs (AAAI 2021)

Reproduce some methods in semi-supervised papers.

Jupyter Notebook corresponding to 'Going with the Flow: An Introduction to Normalizing Flows'

Deep Metric Transfer for Label Propagation with Limited Annotated Data

Pytorch implementations of generative models: VQVAE2, AIR, DRAW, InfoGAN, DCGAN, SSVAE

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

Implementations of normalizing flows using python and tensorflow

Python codes for weakly-supervised learning

[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels

Code for our paper, Neural Network Attributions: A Causal Perspective (ICML 2019).

Benchmarks for semi-supervised domain generalization.

Enhancing the BERT training with Semi-supervised Generative Adversarial Networks

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

Enhancing the BERT training with Semi-supervised Generative Adversarial Networks in Pytorch/HuggingFace

Semi-supervised Learning GAN

Implementation of Normalizing flows on MNIST https://arxiv.org/abs/1505.05770

This project is a semi-supervised approach to detect emotions on faces in-the-wild using GAN