Lightweight Probabilistic Deep Networks

Even though probabilistic treatments of neural networks have a long history, they have not found widespread use in practice. Sampling approaches are often too slow already for simple networks. The size of the inputs and the depth of typical CNN architectures in computer vision only compound this problem. Uncertainty in neural networks has thus been largely ignored in practice, despite the fact that it may provide important information about the reliability of predictions and the inner workings of the network. In this paper, we introduce two lightweight approaches to making supervised learning with probabilistic deep networks practical: First, we suggest probabilistic output layers for classification and regression that require only minimal changes to existing networks. Second, we employ assumed density filtering and show that activation uncertainties can be propagated in a practical fashion through the entire network, again with minor changes. Both probabilistic networks retain the predictive power of the deterministic counterpart, but yield uncertainties that correlate well with the empirical error induced by their predictions. Moreover, the robustness to adversarial examples is significantly increased.Comment: To appear at CVPR 201

Domain Aligned CLIP for Few-shot Classification

Large vision-language representation learning models like CLIP have demonstrated impressive performance for zero-shot transfer to downstream tasks while largely benefiting from inter-modal (image-text) alignment via contrastive objectives. This downstream performance can further be enhanced by full-scale fine-tuning which is often compute intensive, requires large labelled data, and can reduce out-of-distribution (OOD) robustness. Furthermore, sole reliance on inter-modal alignment might overlook the rich information embedded within each individual modality. In this work, we introduce a sample-efficient domain adaptation strategy for CLIP, termed Domain Aligned CLIP (DAC), which improves both intra-modal (image-image) and inter-modal alignment on target distributions without fine-tuning the main model. For intra-modal alignment, we introduce a lightweight adapter that is specifically trained with an intra-modal contrastive objective. To improve inter-modal alignment, we introduce a simple framework to modulate the precomputed class text embeddings. The proposed few-shot fine-tuning framework is computationally efficient, robust to distribution shifts, and does not alter CLIP's parameters. We study the effectiveness of DAC by benchmarking on 11 widely used image classification tasks with consistent improvements in 16-shot classification upon strong baselines by about 2.3% and demonstrate competitive performance on 4 OOD robustness benchmarks.Comment: To appear at WACV 202

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field

A Geometric Multigrid Method for Simulating Deformable Models on Unstructured, Non-nested Mesh Hierarchies

In order to accelerate the solution of linear systems, multigrid methods utilize different levels of discretizations. Transfer operations between different levels are usually straightforward, since most geometric multigrid solver is embedded in structured problem domains. However, the multigrid method in this thesis makes use of barycentric coordinates to cope with unstructured problems. Thereby, the approach is applied to a Finite Element framework simulating deformable models on both linear and quadratic tetrahedral meshes

Estimating Motion from a Single Blurry Image

Estimating motion from a single image source is a heavily ill-posed problem that aims at recovering motion parameters from observed motion blur only. As a result monocular motion estimation is closely related to image deblurring and blind deconvolution. Indeed, since the observed blur is parameterized by the motion, motion parameters can be directly estimated from the blur kernels themselves. Over the last decades a lot of research has been devoted to recovering uniform blur kernels; for motion blur which is not purely translational, however observed blur varies non-uniformly across the image plane, which is why most traditional blind deconvolution techniques are not applicable. In order to recover motion from a single image this work proposes a generative blur model that constructs non-uniform blur kernels according to an affine motion model. By incorporating this model into a variational EM (Expectation Maximization) framework, we are then able to recover the affine parameters by blind deconvolution. As far as the inference is concerned, special care must be taken w.r.t. symmetry because of the directional ambiguity of the motion blur. We finally conduct experiments on ground truth datasets. In particular, we utilize an industrial CNC machine to capture image sequences with high precision. By averaging these capturing sequences, we can synthesize affine non-uniform motion blur, based on which we evaluate the performance of our inference framework

A Geometric Multigrid Method for Simulating Deformable Models on Unstructured, Non-nested Mesh Hierarchies

In order to accelerate the solution of linear systems, multigrid methods utilize different levels of discretizations. Transfer operations between different levels are usually straightforward, since most geometric multigrid solver is embedded in structured problem domains. However, the multigrid method in this thesis makes use of barycentric coordinates to cope with unstructured problems. Thereby, the approach is applied to a Finite Element framework simulating deformable models on both linear and quadratic tetrahedral meshes