Continual Self Supervised Learning through Distillation and Replay

Project Description: Advancing Continual Self-Supervised Learning

Without relying on human annotations, self-supervised learning aims to learn useful representations of input data. When trained offline with enormous amounts of unlabeled data, self-supervised models have been found to provide visual representations that are equivalent to or better than supervised models. However, in continual learning (CL) circumstances, when data is fed to the model sequentially, its efficacy is drastically diminished. Numerous ongoing learning techniques have recently been presented for a variety of computer vision problems. In this study, by utilizing distillation and proofreading, we tackle the extremely challenging problem of continuously learning a usable representation in which input data arrives sequentially. We can prevent severe forgetfulness and continue to train our models by adding a prediction layer that forces the current representations vectors to precisely match the frozen learned representations and an effective selection memory for proofreading previous data.

Objective

The primary objective is to research and implement techniques that enable self-supervised models to learn continually from sequential data streams without significant performance loss, thereby maintaining the quality of learned representations over time.

Key Features

• Distillation: Incorporates a distillation loss where a prediction layer forces the current model’s representations to align with those from a frozen, previously learned model. This helps preserve past knowledge.
• Proofreading with Memory (Replay): Implements an effective memory buffer and selection strategy to store and revisit representative samples from past data distributions, refining learned representations and preventing drift.
• Continual Learning Framework: Designs and evaluates the model within a standard continual learning setup, where tasks or data distributions arrive sequentially.

Methodology

• Leverage state-of-the-art self-supervised learning methods as a base.
• Integrate knowledge distillation to transfer knowledge from a stable teacher model (representing past knowledge) to the continually learning student model.
• Employ a replay buffer (memory) to store past examples, using specific sampling strategies to ensure the buffer remains representative and computationally manageable.
• Evaluate the approach on benchmark computer vision datasets adapted for continual learning scenarios.

Tools and Technologies

• Core Concepts: Self-Supervised Learning (SSL), Continual Learning (CL), Knowledge Distillation, Replay Mechanisms
• Machine Learning Frameworks: PyTorch / TensorFlow (assumed)
• Domains: Computer Vision

Applications

• Robust Representation Learning: Enables learning effective visual representations in dynamic environments where data evolves over time.
• Large-Scale Unlabeled Data: Provides a framework for leveraging large, unlabeled datasets that are naturally sequential or arrive in streams.
• Adaptive AI Systems: Contributes to building AI systems that can adapt to new information without forgetting previously acquired knowledge.

This project demonstrates the effectiveness of combining distillation and replay to mitigate catastrophic forgetting in continual self-supervised learning, paving the way for more adaptive and robust representation learning models.

Language: Python Task: Continual Leaning, Class-IL, Task-IL, Data-IL, Self-Supervised Learning

Code: cssl-distill-replay