Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning

We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimize autonomously hyper-parameters that need to be tuned from any action and/or vision module, treated as a black-box. The learning can take advantage of past experiences (stored in the episodic and procedural memories) in order to warm-start the exploration using a set of hyper-parameters previously optimized from objects similar to the new unknown one (stored in a semantic memory). As example, the system has been used to optimized 9 continuous hyper-parameters of a professional software (Kamido) both in simulation and with a real robot (industrial robotic arm Fanuc) with a total of 13 different objects. The robot is able to find a good object-specific optimization in 68 (simulation) or 40 (real) trials. In simulation, we demonstrate the benefit of the transfer learning based on visual similarity, as opposed to an amnesic learning (i.e. learning from scratch all the time). Moreover, with the real robot, we show that the method consistently outperforms the manual optimization from an expert with less than 2 hours of training time to achieve more than 88% of success

Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Better Single-Source Domain Generalization

Traditional deep learning algorithms often fail to generalize when they are tested outside of the domain of training data. Because data distributions can change dynamically in real-life applications once a learned model is deployed, in this paper we are interested in single-source domain generalization (SDG) which aims to develop deep learning algorithms able to generalize from a single training domain where no information about the test domain is available at training time. Firstly, we design two simple MNISTbased SDG benchmarks, namely MNIST Color SDG-MP and MNIST Color SDG-UP, which highlight the two different fundamental SDG issues of increasing difficulties: 1) a class-correlated pattern in the training domain is missing (SDG-MP), or 2) uncorrelated with the class (SDG-UP), in the testing data domain. This is in sharp contrast with the current domain generalization (DG) benchmarks which mix up different correlation and variation factors and thereby make hard to disentangle success or failure factors when benchmarking DG algorithms. We further evaluate several state-of-the-art SDG algorithms through our simple benchmark, namely MNIST Color SDG-MP, and show that the issue SDG-MP is largely unsolved despite of a decade of efforts in developing DG algorithms. Finally, we also propose a partially reversed contrastive loss to encourage intra-class diversity and find less strongly correlated patterns, to deal with SDG-MP and show that the proposed approach is very effective on our MNIST Color SDG-MP benchmark

The MediaEval 2017 Emotional Impact of Movies Task

Non peer reviewe

Reconstructive and Discriminative Sparse Representation for Visual Object Categorization

International audienceSparse representation was originally used in signal processing as apowerful tool for acquiring, representing and compressinghigh-dimensional signals. Recently, motivated by the great successes it hasachieved, it has become a hot research topic in the domainof computer vision and pattern recognition. In this paper, we propose to adapt sparse representation to the problem of Visual Object Categorization which aims at predicting whether at least one or several objects of some given categories are present in an image. Thus, we have elaborated a reconstructive and discriminative sparserepresentation of images, which integrates a discriminative term, such asFisher discriminative measure or the output of a SVM classifier, intothe standard sparse representation objective function in order tolearn a reconstructive and discriminative dictionary.Experiments carried out on the SIMPLIcity image dataset have clearlyrevealed that our reconstructive and discriminative approach has gained an obviousimprovement of the classification accuracy compared to standard SVMusing image features as input. Moreover, the results have shown that our approach is more efficient than a sparse representation being only reconstructive, which indicates that adding a discriminative term forconstructing the sparse representation is more suitable for thecategorization purpose

The MediaEval 2016 Emotional Impact of Movies Task

ABSTRACT This paper provides a description of the MediaEval 2016 "Emotional Impact of Movies" task. It continues builds on previous years' editions of the Affect in Multimedia Task: Violent Scenes Detection. However, in this year's task, participants are expected to create systems that automatically predict the emotional impact that video content will have on viewers, in terms of valence and arousal scores. Here we provide insights on the use case, task challenges, dataset and ground truth, task run requirements and evaluation metrics

Twitter als Wahlkampfmedium: Modellierung und Analyse politischer Social-Media-Nutzung

Die Veränderung der individuellen politischen Kommunikation ist ein wesentliches Element des Konzepts der Mediatisierung des Politischen. Immer mehr Politikerinnen und Politiker sowie Bürgerinnen und Bürger nutzen digitale Plattformen, um sich politisch auszutauschen und zu informieren. Dabei stellt sich die Frage, inwiefern Politiker/-innen selbst Austauschmöglichkeiten im Netz bieten und somit direkt Kommunikation fördern. Für die vorliegende Studie wurde die Nutzung des Microblogging-Dienstes Twitter durch Politiker/-innen während ausgewählter Landtagswahlkämpfe des Jahres 2011 auf partizipationsermöglichende Elemente hin untersucht. Diese Elemente wurden mithilfe des "Funktionalen Operatorenmodells" systematisiert und kategorisiert. Die Ergebnisse verdeutlichen nicht nur eine individuell ausgeprägte Nutzungsfrequenz der einzelnen Politiker/-innen, sondern auch unterschiedliche Stile der Twitternutzung, die sich als "persönlich-interaktiv" und "thematisch-informativ" klassifizieren lassen. In Hinblick auf deliberative Strukturen ist die Twitterkommunikation im Politiker-Bürger-Dialog hingegen noch ausbaufähig.The change of individual political communication is a crucial element in the debate about the mediatization of politics. More and more politicians as well as citizens make use of digital platforms to exchange their views on political issues and to inform themselves. This raises the question to what extent politicians offer options for digital interaction and thus encourage first-hand communication. This paper examines the use of the microblogging service Twitter by politicians during selected federal state election campaigns in Germany in 2011. The analysis focused on elements that facilitate participation and was conducted by using the "functional operator-model". Data analysis shows that politicians use twitter in either a "personal-interactive" or "topic-informative" style. Regarding deliberative structures of twitter communication, however, there is still much to gain - both on the part of the politicians and of the citizens

panda-gym: Open-source goal-conditioned environments for robotic learning

This paper presents panda-gym, a set of Reinforcement Learning (RL) environments for the Franka Emika Panda robot integrated with OpenAI Gym. Five tasks are included: reach, push, slide, pick & place and stack. They all follow a Multi-Goal RL framework, allowing to use goal-oriented RL algorithms. To foster open-research, we chose to use the open-source physics engine PyBullet. The implementation chosen for this package allows to define very easily new tasks or new robots. This paper also presents a baseline of results obtained with state-of-the-art model-free off-policy algorithms. panda-gym is open-source and freely available at https://github.com/qgallouedec/panda-gym.Comment: NeurIPS 2021 Workshop on Robot Learning: Self-Supervised and Lifelong Learnin

Cell-Free Latent Go-Explore

Under reviewIn this paper, we introduce Latent Go-Explore (LGE), a simple and general approach based on the Go-Explore paradigm for exploration in reinforcement learning (RL). Go-Explore was initially introduced with a strong domain knowledge constraint for partitioning the state space into cells. However, in most real-world scenarios, drawing domain knowledge from raw observations is complex and tedious. If the cell partitioning is not informative enough, Go-Explore can completely fail to explore the environment. We argue that the Go-Explore approach can be generalized to any environment without domain knowledge and without cells by exploiting a learned latent representation. Thus, we show that LGE can be flexibly combined with any strategy for learning a latent representation. We show that LGE, although simpler than Go-Explore, is more robust and outperforms all state-of-the-art algorithms in terms of pure exploration on multiple hard-exploration environments. The LGE implementation is available as open-source at https://github.com/qgallouedec/lge