Guiding InfoGAN with Semi-Supervision

In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN) for image synthesis that leverages information from few labels (as little as 0.22%, max. 10% of the dataset) to learn semantically meaningful and controllable data representations where latent variables correspond to label categories. The architecture builds on Information Maximizing Generative Adversarial Networks (InfoGAN) and is shown to learn both continuous and categorical codes and achieves higher quality of synthetic samples compared to fully unsupervised settings. Furthermore, we show that using small amounts of labeled data speeds-up training convergence. The architecture maintains the ability to disentangle latent variables for which no labels are available. Finally, we contribute an information-theoretic reasoning on how introducing semi-supervision increases mutual information between synthetic and real data

Synthesis and reactions of donor cyclopropanes: efficient routes to cis- and trans-tetrahydrofurans

A detailed study on the synthesis and reactions of silylmethylcyclopropanes is reported. In their simplest form, these donor-only cyclopropanes undergo Lewis acid promoted reaction to give either cis- or trans-tetrahydrofurans, with the selectivity being reaction condition-dependant. The adducts themselves are demonstrated to be an important scaffold for structural diversification. The combination of a silyl-donor group in a donor-acceptor cyclopropane with novel acceptor groups is also discussed

Gesture Recognition: Hand Pose Estimation Ubiquitous computing seminar FS2014

In this report, different vision-based approaches to solve the problem of hand pose estimation are reviewed. The first three methods presented utilize random forest, whereas the last tackles the problem of pose estimation of two strongly interacting hands. Lastly, an outlook at the future of the field is given. ACM Classification: H5.2 [Information interfaces and presentation]

Information Content in the Oxygen A-Band for the Retrieval of Macrophysical Cloud Parameters

Current and future satellite sensors provide measurements in and around the oxygen A-band on a global basis. These data are commonly used for the determination of cloud and aerosol properties. In this paper, we assess the information content in the oxygen A-band for the retrieval of macrophysical cloud parameters using precise radiative transfer simulations covering a wide range of geophysical conditions in conjunction with advance inversion techniques. The information content of the signal with respect to the retrieved parameters is analyzed in a stochastic framework using two common criteria: the degrees of freedom for a signal and the Shannon information content. It is found that oxygen A-band measurements with moderate spectral resolution (0.2 nm) provide two pieces of independent information that allow the accurate retrieval of cloud-top height together with either cloud optical thickness or cloud fraction. Additionally, our results confirm previous studies indicating that the retrieval of cloud geometrical thickness (CGT) from single-angle measurements is not reliable in this spectral region. Finally, a sensitivity study shows that the retrieval of macrophysical cloud parameters is slightly sensitive to the uncertainty in the CGT and very sensitive to the uncertainty in the surface albedo

PeCLR: Self-Supervised 3D Hand Pose Estimation from monocular RGB via Contrastive Learning

Encouraged by the success of contrastive learning on image classification tasks, we propose a new self-supervised method for the structured regression task of 3D hand pose estimation. Contrastive learning makes use of unlabeled data for the purpose of representation learning via a loss formulation that encourages the learned feature representations to be invariant under any image transformation. For 3D hand pose estimation, it too is desirable to have invariance to appearance transformation such as color jitter. However, the task requires equivariance under affine transformations, such as rotation and translation. To address this issue, we propose an equivariant contrastive objective and demonstrate its effectiveness in the context of 3D hand pose estimation. We experimentally investigate the impact of invariant and equivariant contrastive objectives and show that learning equivariant features leads to better representations for the task of 3D hand pose estimation. Furthermore, we show that standard ResNets with sufficient depth, trained on additional unlabeled data, attain improvements of up to 14.5% in PA-EPE on FreiHAND and thus achieves state-of-the-art performance without any task specific, specialized architectures. Code and models are available at https://ait.ethz.ch/projects/2021/PeCLR

Deep Pictorial Gaze Estimation

ISSN:0302-9743ISSN:1611-334