Improving Image Retrieval with User Feedback

2021-02-08T12:05:31+00:00

A problem occurs when an image retrieval method delivers irrelevant results. This post shows how user interaction can be utilized to overcome this problem.

Content based image retrieval is a field in computer vision. The aim is to find the most similar images to a given input image, where the similarity refers to the sem

Improving Image Retrieval with User Feedback2021-02-08T12:05:31+00:00

One Shot Learning: Eure Fragen beantwortet

2020-12-23T12:45:44+00:00

Bei unserem Meetup zum Thema One Shot Learning blieben einige Fragen unbeantwortet. Mai und Sebastian haben sich dieser angenommen und an dieser Stelle beantwortet. Hast du noch weitere Fragen? Dann stelle sie unseren Expert:innen gerne in den Kommentaren!

Bei unserem Meetup zum Thema One Shot Learning blieben einige Fragen unbeantwortet. Mai und Sebastian haben sich dieser angenommen und an dieser Stelle beantwortet. H

One Shot Learning: Eure Fragen beantwortet2020-12-23T12:45:44+00:00

Deep Learning for Mobile Devices with TensorFlow Lite: Train Your Custom Object Detector

2020-12-18T15:44:11+00:00

In the second article of our blog post series about TensorFlow Mobile we are working on quantization-aware model training with the TensorFlow Object Detection API. In the hands-on example we build and train a quantization-aware object detector for cars.

This is the second article of our blog post series about TensorFlow Mo

Deep Learning for Mobile Devices with TensorFlow Lite: Train Your Custom Object Detector2020-12-18T15:44:11+00:00

Inverse Reinforcement Learning and Finding Proper Reward Signals for Snake-like Robots

2020-11-26T11:04:25+00:00

Learning is a process that can be observed across all living creatures – and also machines with the advent of sophisticated hardware and algorithms. This article introduces preference-based inverse reinforcement learning and explains how it can support a snake-like robot to learn to move forward efficiently.

Humans are constantly being taught and acquire knowledge: first by parents, later in school by teachers and at work by colleagues. In fact, learning is a process that

Inverse Reinforcement Learning and Finding Proper Reward Signals for Snake-like Robots2020-11-26T11:04:25+00:00

Deep Learning for Mobile Devices with TensorFlow Lite: Concepts and Architectures

2020-11-13T10:19:37+00:00

This first post tackles some of the theoretical background of on-device machine learning, including quantization and state-of-the-art model architectures for TensorFlow Lite.

The amount of mobile applications making use of some sort of machine learning is quickly increasing, just as the number of potential use cases in this area. Whenever

Deep Learning for Mobile Devices with TensorFlow Lite: Concepts and Architectures2020-11-13T10:19:37+00:00

Deep Learning on Bad Time Series Data: Corrupt, Sparse, Irregular and Ugly

2020-10-22T09:53:03+00:00

How do you train neural networks on time series that are non-uniformly sampled,  irregularly sampled, have non-equidistant timesteps, or have missing or corrupt values? In the following post, I try to summarize and point to effective methods for dealing with such data.

How do you train neural networks on time series that are non-uniformly sampled, irregularly sampled, have non-equidistant timesteps, or have missing or corrupt values

Deep Learning on Bad Time Series Data: Corrupt, Sparse, Irregular and Ugly2020-10-22T09:53:03+00:00