# Meta Learning Based Deception Detection from Speech
## 2 Related Work
### Few-Shot Learning  
The method in our paper is based on the idea of the meta-learning framework or \"learning to learn\" [46], specifically in the field of few-shot learning. The meta-learning framework is based on learning from prior experience with other tasks, i.e., learning how to learn to classify given a set of training tasks such that the model can solve new learning tasks. One of the main challenges in the meta-learning framework is to train an accurate model using only a few training examples given prior experience with similar tasks. This is called few-shot learning. Few-shot learning, is training a model for learning from very few samples and generalizing to many other new examples; most approaches for few-shot learning follow the meta-learning framework. It measures a model's ability to quickly adapt to new environments and tasks using only a few examples and training iterations. For that, the model is trained on a set of tasks in a meta-learning phase, allowing it to adapt quickly to new tasks with just a few examples. Each task consists of a support set used for learning how to solve each specific task and a query set containing further examples of each specific task, which are used to evaluate the performance on each task. A task can be utterly non-overlapping with another; the classes from one task may never appear in another. The model's performance is evaluated by the average test accuracy across the query sets of many testing tasks. As the meta-learning process proceeds, the model parameters are updated based on the training tasks. The loss function is derived from the classification performance on the query set of each of the training tasks based on the knowledge gathered from its support set. The network is given a different task at each time step, so it must learn to discriminate data classes in general rather than specific subsets. A common way of attempting to solve a few-shot learning problem is by using prior knowledge about similarity. This is done by learning class embeddings that tend to separate classes even if they have never been seen before. One of the earlier methods for solving few-shot problems is a pairwise comparator [47; 48] that was developed to classify two examples as belonging or not to the same class based on their similarity, even though the model had never seen those classes before. This method can be adapted to few-shot learning by classifying an example from the query set according to its maximum similarity to an example in the support set. A more elegant way is multi-class comparators [49; 50], which learn a common representation for each class in the training set and match eachnew test example using cosine similarity. [PERSON] et al. [50] propose Prototypical Networks, which average the embeddings of the examples from the same class to compute the class prototype (mean vector). Then a distance metric is used to calculate the similarity (a negative multiple of the Euclidean distance) between each query embeddings to each of the classes' prototypes for finding the most similar class. Alternatively, the few-shot learning problem can be solved by learning parameters that generalize better to similar tasks and can be fine-tuned very quickly when applied to different tasks. An implementation of that approach is Model-Agnostic Meta-Learning (MAML), introduced by [PERSON] et al. [51]. The model is initialized with random weights, and iteratively, for each task in a meta-batch of tasks, it fine-tunes a copy learner using the weights of the primary model (meta-learner). The learner weights are updated using the loss from the query samples in the task by stochastic gradient descent. At the end of each training task, the losses and gradients from the queries are accumulated, the derivative of the mean loss concerning the primary model's weights is computed, and the weights in the primary model are updated. The primary model's weights improve during this process so that the model can fine-tune to other tasks faster.