Search Results for author: Andrew Melnik

Found 10 papers, 7 papers with code

Faces: AI Blitz XIII Solutions

1 code implementation3 Apr 2022 Andrew Melnik, Eren Akbulut, Jannik Sheikh, Kira Loos, Michael Buettner, Tobias Lenze

AI Blitz XIII Faces challenge hosted on www. aicrowd. com platform consisted of five problems: Sentiment Classification, Age Prediction, Mask Prediction, Face Recognition, and Face De-Blurring.

Face Recognition Sentiment Analysis

A Graph-based U-Net Model for Predicting Traffic in unseen Cities

1 code implementation11 Feb 2022 Luca Hermes, Barbara Hammer, Andrew Melnik, Riza Velioglu, Markus Vieth, Malte Schilling

Accurate traffic prediction is a key ingredient to enable traffic management like rerouting cars to reduce road congestion or regulating traffic via dynamic speed limits to maintain a steady flow.

Traffic Prediction

YOLO -- You only look 10647 times

no code implementations16 Jan 2022 Christian Limberg, Andrew Melnik, Augustin Harter, Helge Ritter

With this work we are explaining the "You Only Look Once" (YOLO) single-stage object detection approach as a parallel classification of 10647 fixed region proposals.

Classification Image Classification +3

Transfer Learning with Jukebox for Music Source Separation

1 code implementation28 Nov 2021 Wadhah Zai El Amri, Oliver Tautz, Helge Ritter, Andrew Melnik

In this work, we demonstrate how a publicly available, pre-trained Jukebox model can be adapted for the problem of audio source separation from a single mixed audio channel.

Audio Source Separation Music Source Separation +1

Critic Guided Segmentation of Rewarding Objects in First-Person Views

1 code implementation20 Jul 2021 Andrew Melnik, Augustin Harter, Christian Limberg, Krishan Rana, Niko Suenderhauf, Helge Ritter

This work discusses a learning approach to mask rewarding objects in images using sparse reward signals from an imitation learning dataset.

Imitation Learning

Solving Physics Puzzles by Reasoning about Paths

2 code implementations14 Nov 2020 Augustin Harter, Andrew Melnik, Gaurav Kumar, Dhruv Agarwal, Animesh Garg, Helge Ritter

We propose a new deep learning model for goal-driven tasks that require intuitive physical reasoning and intervention in the scene to achieve a desired end goal.

Modularization of End-to-End Learning: Case Study in Arcade Games

no code implementations27 Jan 2019 Andrew Melnik, Sascha Fleer, Malte Schilling, Helge Ritter

Complex environments and tasks pose a difficult problem for holistic end-to-end learning approaches.

Atari Games

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