Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train more than 12000 models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on seven different data sets. We observe that while the different methods successfully enforce properties ``encouraged'' by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets

A Commentary on the Unsupervised Learning of Disentangled Representations

The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In this paper, we summarize the results of Locatello et al., 2019, and focus on their implications for practitioners. We discuss the theoretical result showing that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases and the practical challenges it entails. Finally, we comment on our experimental findings, highlighting the limitations of state-of-the-art approaches and directions for future research

Investigation of Cooperativity between Statistical Rebinding and the Chelate Effect on DNA Scaffolded Multivalent Binders as a Method for Developing High Avidity Ligands to target the C-type Lectin Langerin

Aufgrund der Fähigkeit von Langerhans Zellen, welche den C-Typ Lektin (CTL) Rezeptor Langerin exprimieren, Antigene zu internalisieren und T-Zellen zu präsentieren, wurde Langerin als attraktives Ziel für neue Immunotherapien erkannt. Langerin kann Pathogene wie z.B. Viren erkennen, die zur Erhöhung der Avidität Kohlenhydratliganden multivalent präsentieren, da die monovalenten Kohlenhydratliganden nur niedrige Affinitäten für Langerin aufweisen. Die natürlichen monovalenten Kohlenhydratliganden besitzen nur niedrige Affinitäten für Langerin. Inspiriert durch die Natur stellt Multivalenz eine Strategie zur Überwindung der schwachen CTL-Kohlenhydrat-Wechselwirkung dar. Im Gegensatz zur hochmultivalenten Präsentation von Liganden mit undefinierter Anordnung hat sich diese Arbeit zum Ziel gesetzt auch die Ökonomie der Liganden zu optimieren, indem Liganden auf einer DNA Gerüststruktur so präsentiert wurden, dass sie die Distanz zwischen den Bindungstaschen des Homotrimers Langerin wiederspiegeln. Eine Untersuchung der relevanten multivalenten Bindungsmechanismen führte zu einer Anordnung der Liganden, die sowohl statistisches Rebinding als auch den Chelate Effekt einbezog. Der Rebinding Effekt wurde als Mittel erkannt, dass nicht nur die Avidität des Liganden an einer Bindungstasche erhöht, sondern auch ausgenutzt werden kann, um den Chelate Effekt zu amplifizieren. Diese Methode stellt eine Möglichkeit dar niedrige oder nicht vorhandene Multivalenzeffekte bei der bivalenten Präsentation von Liganden zu überwinden, wenn hochaffine Liganden nicht zur Verfügung stehen. Eine Kombination dieser Strategie mit der Entwicklung eines neuen selektiven Liganden für Langerin führte zu dem stärksten bekannten Langerinbinder (IC50 = 300 nM). Die Ligand-PNA-DNA Konstrukte wurden selektiv von Langerin exprimierenden Zellen bei nanomolaren Konzentrationen internalisiert und stellen ein System dar, welches in Zukunft für den Transport von Beladungen Anwendung finden könnte.Targeting the C-type lectin (CTL) langerin has received increasing attention as a novel immunotherapy strategy due to the capacity of Langerhans cells, which express langerin, to endocytose and cross-present antigens to T-cells. Langerin recognizes pathogens such as viruses, which present carbohydrates in a multivalent fashion to increase avidity as the monovalent carbohydrate ligands only display low affinity for langerin. Inspired by nature, multivalency has therefore been a key tool for overcoming the low affinities of CTL-carbohydrate interactions. In contrast to highly multivalent ligand presentation with undefined arrangements this work strove to optimize ligand economy by designing bivalent ligands that take the distance between the binding sites of the homotrimeric langerin into consideration by precise arrangement of ligands on DNA-based scaffolds. Studying the multivalent mechanisms at work led us to the design of ligands that take both statistical rebinding and the chelate effect into account. The rebinding effect was recognized as a tool that not only increases ligand avidity at a single binding site but in addition can be exploited to amplify the chelate effect. This method provides a solution for overcoming the low or non-existing multivalency effects when bivalently presenting low affinity ligands on a rigid scaffold if high affinity ligands are unavailable. A combination of this arrangement strategy with the development of a first langerin selective glycomimetic ligand led to the most potent molecularly defined langerin binder to date (IC50 = 300 nM). The ligand-PNA-DNA constructs were selectively internalized by langerin expressing cells at nanomolar concentrations and constitute a delivery platform for the future transport of cargo to Langerhans cells

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation

The idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train over 14000 models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on eight data sets. We observe that while the different methods successfully enforce properties “encouraged” by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, different evaluation metrics do not always agree on what should be considered “disentangled” and exhibit systematic differences in the estimation. Finally, increased disentanglement does not seem to necessarily lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.ISSN:1532-4435ISSN:1533-792

Rational design of an DNA-scaffolded high-affinity binder for langerin

Binders of Langerin could target vaccines to Langerhans cells for improved therapeutic effect. As Langerin has only low affinity for monovalent glycan ligands, highly multivalent presentation has previously been key for targeting. Aiming to reduce the amount of ligands required, we rationally designed molecularly defined high affinity binders based on the precise display of glycomimetic ligands (Glc2NTs) on DNA-PNA scaffolds. Rather than mimicking Langerin?s homotrimeric structure with a C3-symmetrical scaffold, we devised a strategy to improve readily accessible, easy to design bivalent binders. The method considers the requirements for bridging sugar binding sites and statistical rebinding as a means to both strengthen the interactions at single binding sites and amplify the avidity enhancement provided by chelation. The method enabled a 1150-fold net improvement over the affinity of the free ligand and provided a nanomolar binder (IC50 = 300 nM) for specific internalization by Langerin expressing cells

Rational Design of a DNA‐Scaffolded High‐Affinity Binder for Langerin

Binders of langerin could target vaccines to Langerhans cells for improved therapeutic effect. Since langerin has low affinity for monovalent glycan ligands, highly multivalent presentation has previously been key for targeting. Aiming to reduce the amount of ligand required, we rationally designed molecularly defined high‐affinity binders based on the precise display of glycomimetic ligands (Glc2NTs) on DNA‐PNA scaffolds. Rather than mimicking langerin's homotrimeric structure with a C3‐symmetric scaffold, we developed readily accessible, easy‐to‐design bivalent binders. The method considers the requirements for bridging sugar binding sites and statistical rebinding as a means to both strengthen the interactions at single binding sites and amplify the avidity enhancement provided by chelation. This gave a 1150‐fold net improvement over the affinity of the free ligand and provided a nanomolar binder (IC50=300 nM) for specific internalization by langerin‐expressing cells.Peer Reviewe