PaddlePaddle
Train self data, overfit problem.
I train video classify model with AttentionLSTM, and I got Hit@1: 1.0 in train but got Hit@1: 0.40 in valid.
When training a model, especially for tasks like video classification, encountering a significant disparity between training and validation performance, such as a perfect Hit@1 score of 1.0 in training but only 0.40 in validation, is indicative of overfitting. Overfitting occurs when your model learns the training data too well, including its noise and outliers, to the extent that it performs poorly on unseen data. Here are some strategies to mitigate overfitting:
1. Data Augmentation
For video data, you can apply various augmentation techniques like random cropping, rotation, flipping, changes in brightness/contrast, and temporal segment selection. Data augmentation increases the diversity of your training data, helping the model generalize better.
2. Regularization
Apply regularization techniques such as L1/L2 regularization, which add a penalty on the size of the coefficients to the loss function, and dropout in your AttentionLSTM architecture. Dropout randomly ignores a subset of neurons during training, which helps prevent the network from becoming too dependent on any single neuron.
3. Reduce Model Complexity
If your model is too complex, consider simplifying it by reducing the number of layers or the number of units in each layer. A simpler model has less capacity to memorize the training data and is forced to learn more general patterns.
4. Early Stopping
Monitor the model's performance on the validation set after each epoch of training and stop training when the validation loss begins to increase. This technique prevents the model from continuing to learn from the training data to the point of overfitting.
5. Increase Training Data
More data can help the model learn more general patterns. If possible, collect more video data for training or consider using data from similar domains.
6. Use Pretrained Models
Transfer learning from pretrained models can also be effective. You can start with a model that has been trained on a large, diverse video dataset, then fine-tune it on your dataset. This approach leverages the general features learned by the pretrained model, which can improve generalization.
7. Cross-validation
Instead of a single train-validation split, use k-fold cross-validation to ensure that your model's performance is robust across different subsets of the data.
8. Batch Normalization
Applying batch normalization after the activation of each layer can help by normalizing the inputs to layers within the network. It can improve the stability and speed of the training process, and sometimes help with generalization.
Implementing a combination of these strategies should help in reducing overfitting and improving your model's performance on the validation set. It's important to experiment and monitor the effects of each change to find the right balance for your specific problem.
