Search Results for "data masking"
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Showing 14 open source projects for "data masking"
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pgsync
Postgres to Elasticsearch/OpenSearch sync
pgsync is a lightweight tool for syncing Postgres databases across environments, such as from production to staging. It allows selective table syncing, data masking, and parallel copying for fast and safe data migration. pgsync is ideal for developers who need realistic test data without exposing sensitive information. -
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vJEPA-2
PyTorch code and models for VJEPA2 self-supervised learning from video
...This objective encourages the model to learn semantics, motion, and long-range structure without the shortcuts that pixel-level losses can invite. The architecture is designed to scale: spatiotemporal ViT backbones, flexible masking schedules, and efficient sampling let it train on long clips while remaining stable. Trained representations transfer well to downstream tasks such as action recognition, temporal localization, and video retrieval, often with simple linear probes or light fine-tuning. The repository typically includes end-to-end recipes—data pipelines, augmentation policies, training scripts, and evaluation harnesses. -
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JEPA
PyTorch code and models for V-JEPA self-supervised learning from video
...This makes learning focus on semantics and structure, yielding features that transfer well with simple linear probes and minimal fine-tuning. The repository provides training recipes, data pipelines, and evaluation utilities for image JEPA variants and often includes ablations that illuminate which masking and architectural choices matter. Because the objective is non-autoregressive and operates in embedding space, JEPA tends to be compute-efficient and stable at scale. The approach has become a strong alternative to contrastive or pixel-reconstruction methods for representation learning. -
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OpenAI Privacy Filter
Bidirectional token-classification model for identifiable info
OpenAI Privacy Filter is an open-weight machine learning model designed to detect and mask personally identifiable information in text with high efficiency and contextual awareness. It operates as a bidirectional token classification system that labels sensitive data in a single forward pass rather than generating text sequentially, enabling fast processing for large datasets. The model supports long-context inputs, allowing it to analyze extensive documents without chunking, which improves... -
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The auditory modeling toolbox (AMT) is a Matlab/Octave toolbox for the development and application of auditory computational models. Over 50 auditory models implemented in Matlab, Octave, C, C++, and Python can be run from Matlab and Octave, on Windows and Linux. The AMT provides a well-structured in-code documentation, includes auditory data required to run the models. It integrates functionality to reproduce the model predictions. Model implementations can be evaluated in two stages,...
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SMILI
Scientific Visualisation Made Easy
The Simple Medical Imaging Library Interface (SMILI), pronounced 'smilie', is an open-source, light-weight and easy-to-use medical imaging viewer and library for all major operating systems. The main sMILX application features for viewing n-D images, vector images, DICOMs, anonymizing, shape analysis and models/surfaces with easy drag and drop functions. It also features a number of standard processing algorithms for smoothing, thresholding, masking etc. images and models, both with... -
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PII-Blackout
100% offline, AI-powered PDF redaction
PII Blackout — 100% Offline, Automated PDF & Image Redaction Protecting sensitive data shouldn't mean risking your privacy by uploading files to online tools. PII Blackout is a powerful, local-first desktop application that automates the tedious process of finding and masking Personally Identifiable Information (PII) with military-grade security. Key Features: Smart Auto-Detection & Redaction Manually searching for emails, phone numbers, tax IDs, and names is a thing of the past. ... -
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REDACT
Erase everything. Leave nothing.
REDACT 4.0.0 is an open-source Windows anti-forensics & artifact sanitization suite that permanently destroys 285 system artifacts — browser history, registry traces, NTFS change journals, Windows Recall AI snapshots, live RAM, and encrypted volume key material. New in 4.0.0: Auto-Trigger Engine — 7 triggers: USB Panic, Login Failure, Dead Man's Switch, VPN Disconnect, WiFi Change, Time Window, Forensic Tool Detection. BitLocker and VeraCrypt header destruction renders encrypted... -
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iJEPA
Official codebase for I-JEPA
...This objective sidesteps generative pixel losses and avoids heavy negative sampling, producing features that transfer strongly with linear probes and minimal fine-tuning. The design scales naturally with Vision Transformer backbones and flexible masking strategies, and it trains stably at large batch sizes. i-JEPA’s predictions are made in embedding space, which is computationally efficient and better aligned with downstream discrimination tasks. The repository provides training recipes, data pipelines, and evaluation code that clarify which masking patterns and architectural choices matter most. -
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Jraph
A Graph Neural Network Library in Jax
Jraph (pronounced “giraffe”) is a lightweight JAX library developed by Google DeepMind for building and experimenting with graph neural networks (GNNs). It provides an efficient and flexible framework for representing, manipulating, and training models on graph-structured data. The core of Jraph is the GraphsTuple data structure, which enables users to define graphs with arbitrary node, edge, and global attributes, and to batch variable-sized graphs efficiently for JAX’s just-in-time compilation. The library includes a comprehensive set of utilities for batching, padding, masking, and partitioning graph data, making it ideal for distributed and large-scale GNN experiments. ... -
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RoBERTa for Chinese
RoBERTa Chinese pre-training model: RoBERTa for Chinese
...It provides TensorFlow and PyTorch-compatible model releases trained on large-scale Chinese text. The project follows the main RoBERTa training ideas, including removing next sentence prediction, using more diverse data, training longer, increasing batch size, and tuning optimization settings. Its training data includes news, community discussion, encyclopedia content, and other broad Chinese text sources. The repository also describes whole word masking for Chinese and provides examples for loading and fine-tuning models on sentence-pair matching tasks. ... -
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MAE (Masked Autoencoders)
PyTorch implementation of MAE
MAE (Masked Autoencoders) is a self-supervised learning framework for visual representation learning using masked image modeling. It trains a Vision Transformer (ViT) by randomly masking a high percentage of image patches (typically 75%) and reconstructing the missing content from the remaining visible patches. This forces the model to learn semantic structure and global context without supervision. The encoder processes only the visible patches, while a lightweight decoder reconstructs the... -
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COCO Annotator
Web-based image segmentation tool for object detection & localization
...Once installed, users can manually define regions in an image and creating a textual description. Generally, objects can be marked by a bounding box, either directly, through a masking tool, or by marking points to define the containing area. COCO Annotator allows users to annotate images using free-form curves. -
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The csvdatamix project aims to randomize CSV input data files in order to conceal the original state of the data. Similar to data masking or data transformation. Also has mapping abilities to translate back to the original state of the data.
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