Anker's P1i offers an easy setup, Google TV and fold out speakers, but lacks brightness. Anker's Soundcore projectors have become an attractive option for buyers thanks to models like the P1 and Nebula X1 that combine performance and portability. Now, the company has added affordability to that equation with its latest model, the $369 P1i . Instead of being detachable like on the P1, its speakers fold out toward listeners, promising better and louder sound than most cheap projectors. The P1i also delivers 1080p video, Google TV for streaming and the same easy screen fit setup as other Anker projectors.

TVs are bigger and better than ever. These are my favorite screens that come in extra-large sizes, from affordable to ostentatious. TVs have (literally) never been bigger. TV brands like LG, Samsung, TCL, Sony, and others have gotten the message buyers have been sending for some time now: Go big or go home. The demand has led to exponential growth for the big-screen TV--virtually every brand I talk to cites this as their fastest-growing segment--and thanks to a dizzying array of major leaps in display technology across brands, the best large TVs have never looked better cost less.

In this section, we further investigate the effectiveness of the proposed method when the feature dimensions of the student and teacher are different. In our experiments, we find that simply initializing different projectors with different seeds and the default initialization method of linear layer in Pytorch is sufficient to yield good performance. Therefore, we stick to this strategy to make the proposed method as simple as possible. Experimentalresults showthatmixing differentinitialization methods hasaslightimpact ontheperformance and is a potential way to further improve the distillation performance. We can see that the training times and memory usages of our method will slightly increase with theincrease ofthenumber ofprojectors.

In knowledge distillation, previous feature distillation methods mainly focus on the design of loss functions and the selection of the distilled layers, while the effectofthefeatureprojector between thestudent andtheteacher remains underexplored.

CIFAR-10-L T, CIFAR-100-L T, ImageNet-100-L T, and Places-L T are 5, 80, 50, and 182 respectively. Our default training set of each dataset is summarized in Table 8.

We show how this idealized loss can be reformulated to a functionally equivalent loss that is more efficient to compute.