Transfer Learning Lab
A CPU-friendly set of experiments on transferability between tasks in neural networks.
The lab uses controlled MNIST and Fashion-MNIST variants with a shared CNN so each experiment can compare directly against a from-scratch baseline. It studies pretraining and fine-tuning, layer transferability, task-transfer matrices, negative transfer, catastrophic forgetting, multi-task synergy and interference, modular composition, and self-supervised pretext tasks.
A key goal is to make broad transfer-learning claims concrete enough to falsify. The executed notebooks include machine-checked claim assertions and experiments such as asymmetric task transfer, poisoned and frozen representations, replay against forgetting, gradient conflict between auxiliary tasks, and a pretrained encoder reused inside a compound two-digit architecture.