University of Pisa
M.Sc. Computer Science, Artificial Intelligence
Continual learning 2022/23
Authors
- Andrea Iommi - [email protected]
- Classical acGAN in offline settings
- Classical acGAN in online settings
- acGAN with join retrain
- acGAN with replay alignment
The aim of this small project is to have a deal with some Continual learning (CL) methods in the Axuliarity Conditional Adversarial Networks (acGAN). Typically, in a standard setting, we train a network passing the whole dataset at the same time, but this kind of approach is not always possible: maybe all classes in the database are not available at the same time, the dataset is extremely large and cannot suit in RAM or the environment provide us one class per time and for privacy setting the data cannot be stored and so on.
The CL methods implemented give us a solution. More exactly, we explored the join retrain (family of rehearsal methods) and the replay alignment (family of regularization methods). The first one exploits a buffer to avoid that the network forgets the previous information acquired. The peculiarity is that the buffer is non composed by the input taken from the previous experiences (in this case we are talking of images) but are self generated by the network before starting the next experience. Regarding the second method, it adopts a more implicit mechanism. It adds the "alignment" loss that aims to keep the output of the network in the previous experience equal to the output of the network in the current one.
All implementations are preformed from scratch in pytorch. The theory and details of the methods presented can be found in the original papaer (liked above).
We tried different configurations of hyperparameter in order to choose the best ones. In particular:
| Hyperparameters | Values | Description |
|---|---|---|
| num_classes | 10 | |
| img_size | 32 | |
| channels | 1 | |
| n_epochs | 30/50/100 | Under 30 there was under-fitting, and with 100 we obtained good results |
| batch_size | 32/64 | The original paper suggests 64, but with some experiments 32 seems more suitable |
| embeddings | 100/150/200 | The results were very similarity, we think that is not so much crucial for this kind of purpose |
| lr | 7e-5/1e-4/1e-3 | (Also in this way the original paper suggests 1e-4 but since we've shrunk the batch_size, we reduced the lr |