For scientists, random fluctuations, or noise is undesirable.Although typically assumed to degrade performance, it can sometimes improve information processing in non-linear systems. In this post we’ll see some examples where the noise can be used as an advantage.Recent works have shown that, by allowing some inaccuracy when training deep neural networks, not only the training performance but also the accuracy of the model can be improved.Neural networks are capable of learning output functions that can change wildly with small changes in input. Adding noise to inputs randomly is like telling the network to not change the output in a ball around your exact input.By limiting the amount of information in a network, we force it to learn compact representations of input features.RL is an area of machine learning that assumes there is an agent situated in an environment. At each step, the agent takes an action, and it receives an observation and reward from the environment.An RL algorithm seeks to maximize the agent’s total reward, given a previously unknown environment, through a learning process that usually involves lots of trial and error.To understand the challenge with exploration in Deep RL systems think about researchers that spend lot of times in a Lab without producing any practical application. Equivalently, RL agents can spend a huge amount of resources without converging to a local optimum.OpenAI proposes a technique called Parameter-Space-Noise, that introduces noises in the model policy parameters at the beginning of each episode.Other approaches were focused on what is known as Action-Space-Noise which introduce noise to change the likelihoods associated with each action the agent might take from one moment to the next.The initial results of the Parameter-Space-Noise model proved to be really promising. The technique helps algorithms explore their environments more effectively, leading to higher scores and more elegant behaviors. More details can be found in the research paper.The important thing to remember is that adding noise was used as an advantage to boost the exploration performance of reinforcement learning algorithms.Boosting recognition isn’t as simple as throwing more labeled images at these systems. Indeed, manually annotating a large number of images is an expensive and time consuming process.Facebook researchers and engineers have addressed this by training image recognition networks on large sets of public images with hashtags.Since people often caption their photos with hashtags, it woul’d be a good source of training data for models.Facebook developed new approaches that are tailored for doing image recognition experiments using hashtag supervision. This study is described in detail in “Exploring the Limits of Weakly Supervised Pretraining”On the COCO object-detection challenge, it has been shown that the use of hashtags for pretraining can boost the average precision of a model by more than 2 percent.Noise should not be our enemy ! It isn’t always an unwanted disturbance and can often be used as an advantage and even serve as a valuable research tool. If anyone tries to tell you otherwise, well, just give him the examples we presented ...Stay tuned and if you liked this article, please leave a 👏![1] Weakly-supervised-pretraining: https://research.fb.com/publications/exploring-the-limits-of-weakly-supervised-pretraining/[2] Better Exploration with Parameter Noise: https://blog.openai.com/better-exploration-with-parameter-noise/From a quick cheer to a standing ovation, clap to show how much you enjoyed this story.MSc in Machine Learning (MVA) @ ENS Paris-SaclaySharing concepts, ideas, and codes.