We present an approach to effectively use millions of images with noisy annotations in conjunction with a small subset of cleanly-annotated images to learn powerful image representations. One common approach to combine clean and noisy data is to first pre-train a network using the large noisy dataset and then fine-tune with the clean dataset. We show this approach does not fully leverage the information contained in the clean set.

Sharing information among multiple learning agents can accelerate learning. It could be particularly useful if learners operate in continuously changing environments, because a learner could benefit from previous experience of another learner to adapt to their new environment. Such group-adaptive learning has numerous applications, from predicting financial time-series, through content recommendation systems, to visual understanding for adaptive autonomous agents. Here we address the problem in the context of online adaptive learning.

Precomputing high-quality sample points has been shown to be a useful technique for Monte Carlo integration in rendering; doing so allows optimizing properties of the points without the performance constraints of generating samples during rendering. A particularly useful property to incorporate is stratification across elementary intervals, which has been shown to reduce error in Monte Carlo integration. This is a key property of the recently-introduced progressive multi-jittered, pmj02 and pmj02bn points [Christensen et al.

Given a random pair of images, a universal style transfer method extracts the feel from a reference image to synthesize an output based on the look of a content image. Recent algorithms based on second-order statistics, however, are either computationally expensive or prone to generate artifacts due to the trade-off between image quality and run-time performance. In this work, we present an approach for universal style transfer that learns the transformation matrix in a data-driven fashion.

Throughput-oriented architectures, such as GPUs, can sustain three orders of magnitude more concurrent threads than multicore architectures. This level of concurrency pushes typical synchronization primitives (e.g., mutexes) over their scalability limits, creating significant performance bottlenecks in modules, such as memory allocators, that use them.

We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis.

Traditional optical manufacturing poses a great challenge to near-eye display designers due to large lead times in the order of multiple weeks, limiting the abilities of optical designers to iterate fast and explore beyond conventional designs. We present a complete near-eye display manufacturing pipeline with a day lead time using commodity hardware.

In this chapter, we discuss a pragmatic approach to real-time supersampling that extends commonly used temporal antialiasing techniques with adaptive ray tracing. The algorithm conforms to the constraints of a commercial game engine, removes blurring and ghosting artifacts associated with standard temporal antialiasing, and achieves quality approaching 16× supersampling of geometry, shading, and materials within the 16 ms frame budget required of most games.

We find intersections between a ray and a nonplanar bilinear patch using simple geometrical constructs. The new algorithm improves the state of the art performance by over 6X and is faster than approximating a patch with two triangles.

This book is a collection of articles focused on ray tracing techniques for serious practitioners. Like other "gems" books, it focuses on subjects commonly considered too advanced for introductory texts, yet rarely addressed by research papers.