- Overfit a single batch
- Run with a high number of epochs
- Set seeds
- Rebalance the dataset
- Use a neutral class
- Set the bias of the output layer
- Deliberately overfit in case of physical simulations
- Tune the learning rate
- Use fast data pipelines
- Use data augmentation
- Train an AutoEncoder on unlabeled data, use latent space representation as embedding
- Utilize embeddings from other models
- Use embeddings to shrink data
- Use checkpointing
- Write custom training loops
- Set hyperparameters appropriately
- Use EarlyStopping
- Use transfer-learning
- Employ data-parallel training
- Use sigmoid activation for multi-label tasks