# Dynamic Relevance-Weighting-Based Width-Adaptive Auto-Encoder
## 2 Literature Review
### Unsupervised Learning Techniques  
The width of a stacked fully connected AE is learned with respect to each layer by pruning irrelevant neurons in [20]. Irrelevant neurons are identified by comparing the neuron reconstruction error with a predefined threshold. The reconstruction error is a measure of how well a model can reconstruct the original input data from its encoded representation. It is used as a criterion for selecting the most informative features and pruning unnecessary units. Therefore, if the neuron reconstruction error is 20 times greater than the threshold, the related neuron and its input data is prohibited from participating in the training process. The performance evaluation is based on their ability to reconstruct the input data with the least amount of error. In [17], the Neurogenesis Deep Learning (NDL) is introduced. It involves adding new neurons at any layer while training a stacked deep AE. Specifically, the reconstruction error (RE) is used to determine if an AE (pair of encode/decode layers) is able to represent that input set. In fact, If RE is higher than a predefined threshold, the AE does not have enough neurons to reconstruct the data. Therefore, a neuron is added to the AE under consideration. As such, neurons are incrementally added until the number of neurons reaches a maximum user-defined value. Consequently, NDL is trained with the whole training data when a new neuron is added. The process is repeated until the RE falls below the predefined threshold or when the maximum number of neurons for the current layer is reached. Another study that used a bias-variance regulation method is the \"Self-evolving Autoencoder Embedded Q-Network\" (SAQN) [21], which is an innovative combination of self-adaptive autoencoders and reinforcement learning. The autoencoder progressively adapts as the agent engages with its surroundings. This versatility enables the autoencoder to proficiently capture a wide array of observations, expressing them effectively in its latent space. The evolution process involves node growth, integrating additional nodes to retain previously acquired knowledge, and ensuring a comprehensive representation of the environment. Node Pruning involves removing fewer contributory nodes to maintain a manageable and efficient latent space. This adaptive method ensures that the autoencoder remains responsive to new information while preserving essential knowledge. A core component of SAQN is the bias-variance regulation method, which balances the model's complexity with generalization ability. This technique optimally adjusts the reinforcement learning agent's response to environmental changes by regulating node growth and pruning. The SAQN adheres to the same bias-variance regulatory strategy as the DEVDAN [18] model. Nevertheless, DEVDAN employs discriminative and generative training, whereas SAQN exclusively employs generative training. An evolutionary approach is used in [22] to learn AE architecture. The AE configuration is represented by a chromosome on which crossover and mutation operations are performed. Precisely, the chromosome is composed of a set of 14 genes organized into five groups. The first group consists of one gene to set the type of AE, the second group includes one gene to estimate the number of layers, the third group includes three genes to set the number of units per layer, the fourth group includes six genes to define the activation functions, and, lastly, the fifth group includes one gene to evaluate the loss function. Another study [23] employs evolutionary search methodology to estimate the topology of an AE. However, the depth is constrained to five. Similarly, the width is constrained to be a multiple of 50 which is larger than 50, and smaller than 1000. The fitness function of the evolutionary search is defined as the inverse of the AE reconstruction loss. To optimize the fitness function, a mutation is applied on the parent genotype to give an offspring which increments the depth.