In the context of transformers, noise reduction typically refers to improving the quality of the generated outputs or predictions by reducing the influence of random or irrelevant information. Here are some methods commonly used to reduce noise in transformers:
Teacher Forcing and Scheduled Sampling: In sequence-to-sequence tasks, like machine translation, during training, the model is often fed with correct tokens (words) from the target sequence as input, a technique known as "teacher forcing." However, during inference, the model may receive its own predictions as input, which can lead to error accumulation. Scheduled sampling involves gradually transitioning from teacher forcing to using the model's own predictions during training, helping it better handle its own errors.
Label Smoothing: This technique involves modifying the ground truth labels slightly during training. Instead of using a hard one-hot encoded label, label smoothing uses a soft distribution that assigns a small probability to incorrect tokens. This helps to prevent the model from becoming too certain about incorrect predictions, thus reducing overfitting and improving generalization.
Beam Search and Sampling: During decoding (generating sequences), beam search is commonly used to explore multiple possible sequences and select the most likely one. However, beam search tends to favor overly generic or repetitive outputs. Techniques like "nucleus sampling" (also known as top-p sampling) or "temperature scaling" can be applied to sampling methods to make them more diverse and less deterministic, reducing the noise in the generated outputs.
Length Normalization and Penalties: When using beam search, longer sequences tend to have lower probabilities due to the multiplication of probabilities at each step. Length normalization involves dividing the log-likelihood by the length of the sequence to counteract this effect. Additionally, length penalties can be applied to favor longer or shorter sequences, depending on the desired trade-off between fluency and informativeness.
Regularization Techniques: Adding regularization terms to the training loss, such as dropout, can help the model become more robust to noise in the input data. Dropout randomly disables some units during training, forcing the model to learn more robust and generalized representations.
Adversarial Training: Adversarial training involves training a secondary model, called a discriminator, to distinguish between generated and real data. The generator model then learns to generate more realistic outputs to fool the discriminator. This can help in reducing the noise in generated samples.
Ensemble Methods: Training and combining multiple versions of the same model with slightly different initializations or architectures can help reduce the impact of noisy predictions. Ensemble methods exploit the diversity of predictions from different models to arrive at a more accurate and stable final prediction.
Remember that the choice of noise reduction method depends on the specific problem you're working on and the characteristics of your data. It's often a matter of experimentation to find the best combination of techniques for noise reduction in transformers.