Compute isessential tomodern machine learning applications, andmorecompute typically yields better results. It is thus important to compare our method's compute requirements to competing methods. Table 10: Training compute requirements for our diffusion models compared to StyleGAN2 and BigGAN-deep. Underreasonablesettingsforβt andT,thedistribution q(xT) is nearly an isotropic Gaussian distribution, so samplingxT is trivial. In particular, they do not directly parameterizeµθ(xt,t) as a neural network,butinsteadtrainamodel ϵθ(xt,t)topredictϵfromEquation3.

Keep an eye out for dragonfly swarms. Breakthroughs, discoveries, and DIY tips sent every weekday. When you think of migration, the first creature to pop into your head are probably birds . The second will likely be whales, and the third might be monarch butterflies (). You probably have no idea that migratory dragonfly species exist--and that's because even researchers don't know a whole lot about them. And yet, North America may have up to 18 migratory dragonfly species .

FID evaluated over 10k samples instead of 50k for efficiency. It is thus important to compare our method's compute requirements to competing methods. BigGAN-deep with the same or lower compute budget. We include communication time across two machines whenever our training batch size doesn't We find that a naive implementation of our models in PyTorch 1.7 is very inefficient, utilizing only Table 7: Throughput of our ImageNet models, measured in Images per V100-sec. In addition, we can train for many fewer iterations while maintaining sample quality superior to BigGAN-deep.

In recent years, the monarch butterfly's iconic migration patterns have come under threat from a number of factors, from climate change to pesticide use. To track trends in their populations, scientists as well as citizen scientists must identify individuals accurately. This is uniquely key for the study of monarch butterflies because there exist other species of butterfly, such as viceroy butterflies, that are "look-alikes" (coined by the Convention on International Trade in Endangered Species of Wild Fauna and Flora), having similar phenotypes. To tackle this problem and to aid in more efficient identification, we present MonarchNet, the first comprehensive dataset consisting of butterfly imagery for monarchs and five look-alike species. We train a baseline deep-learning classification model to serve as a tool for differentiating monarch butterflies and its various look-alikes. We seek to contribute to the study of biodiversity and butterfly ecology by providing a novel method for computational classification of these particular butterfly species. The ultimate aim is to help scientists track monarch butterfly population and migration trends in the most precise and efficient manner possible.