PrismSSL: One Interface, Many Modalities; A Single-Interface Library for Multimodal Self-Supervised Learning
This provides a practical tool for researchers and practitioners to streamline SSL experimentation across multiple modalities, though it is incremental as it packages existing methods rather than introducing new algorithms.
The authors tackled the problem of fragmented self-supervised learning (SSL) implementations across modalities by developing PrismSSL, a Python library that unifies state-of-the-art SSL methods for audio, vision, graphs, and cross-modal settings in a single, modular codebase, enabling easy installation, configuration, and extension with features like distributed training and a graphical dashboard.
We present PrismSSL, a Python library that unifies state-of-the-art self-supervised learning (SSL) methods across audio, vision, graphs, and cross-modal settings in a single, modular codebase. The goal of the demo is to show how researchers and practitioners can: (i) install, configure, and run pretext training with a few lines of code; (ii) reproduce compact benchmarks; and (iii) extend the framework with new modalities or methods through clean trainer and dataset abstractions. PrismSSL is packaged on PyPI, released under the MIT license, integrates tightly with HuggingFace Transformers, and provides quality-of-life features such as distributed training in PyTorch, Optuna-based hyperparameter search, LoRA fine-tuning for Transformer backbones, animated embedding visualizations for sanity checks, Weights & Biases logging, and colorful, structured terminal logs for improved usability and clarity. In addition, PrismSSL offers a graphical dashboard - built with Flask and standard web technologies - that enables users to configure and launch training pipelines with minimal coding. The artifact (code and data recipes) will be publicly available and reproducible.