Cyclic learning rate for advanced artificial intelligence
Instead of a constant or decreasing learning rate, a cyclic learning rate fluctuates between a lower bound and an upper bound, which can lead to faster convergence.
What it does: Instead of applying a constant learning rate during training, cyclic learning rates fluctuate between a minimum and a maximum value. This fluctuation can help the model avoid local low points in the loss landscape.
Pros: Can lead to faster convergence and better final performance. Reduces the need to manually adjust learning speed.
Self-study with a loud student
In this technique, a student model is trained with the predictions of a teacher model and the data is supplemented with noise.
What it does: This technique uses a well-trained model (“teacher”) to generate predictions from unlabelled data. These predictions, possibly accompanied by additional noise, are then used as “pseudo-labels” to further train another model (the “student”). The process can be iterative, with the student becoming the teacher in the next cycle.
Pros: Can lead to performance improvements by using unlabelled data. This is particularly beneficial when there is little labelled data but you have access to a large amount of unlabelled data.
capsule networks Capsule
networks (CapsNets) capture spatial hierarchies between objects, making them resilient to spatial changes.
What it does: Traditional neural networks sometimes have difficulty recognizing spatial hierarchies between objects. Capsule networks aim to solve this problem by ensuring that the network recognizes patterns in spatial variations. In this context, a “capsule” is a group of neurons that captures a specific feature and its various properties.
Advantages: Capsule networks are more resilient to adversarial attacks and better preserve spatial hierarchies in imagery. You can recognize the same object in different poses and spatial configurations.