![]() ![]() ![]() ![]() In the Effects layout, for example, that function can be used to change which three effects are controlled by the physical footswitches, while in the Scenes layout it offers a different set of scenes across the switches. Usually a press-and-hold on the left or right footswitches will move to the next set of three footswitches in the 12. While there are only three physical footswitches, there are many ways to use them: 12 footswitches – each with one Tap and one Hold function – are defined in the eight different switch ‘layouts’. You could set up one preset with all the Scenes you need for a song (or even a full set) and switch Scenes when needed.įractal says that the large 800x480 colour display is optimised for readability in the most difficult conditions. Scenes may be the way to go when using the unit live as switching is more seamless than reloading an entire preset. You can have up to eight of these per preset, each storing the engaged or bypassed status of the blocks and their active Channel for a range of alternative versions of the preset’s rig, maybe with different levels of gain for an amp and/or different effects engaged – there are myriad possibilities. Each block also has up to four Channels, so a Drive block could provide access to four different overdrive pedals or perhaps the same pedal with four different settings.Īll of that comes into play when you’re switching between Scenes, which are like presets within a preset. Each block has access to a range of different models: there are 269 amps while the Drive block has 43.Īs for cabinets, there’s every factory cab from the Axe-Fx III plus 1,024 User Cab memory locations. Additionally, fractal networks exhibit an anytime property: shallow subnetworks provide a quick answer, while deeper subnetworks, with higher latency, provide a more accurate answer.There are some things missing, such as a vocoder, but the essential stuff is all present and correct – there’s just less of it, like two Drive and two Delay blocks, instead of four each on the Axe-Fx III. Such regularization allows extraction of high-performance fixed-depth subnetworks. We note similarities with student-teacher behavior and develop drop-path, a natural extension of dropout, to regularize co-adaptation of subpaths in fractal architectures. Rather, the key may be the ability to transition, during training, from effectively shallow to deep. In experiments, fractal networks match the excellent performance of standard residual networks on both CIFAR and ImageNet classification tasks, thereby demonstrating that residual representations may not be fundamental to the success of extremely deep convolutional neural networks. These networks contain interacting subpaths of different lengths, but do not include any pass-through or residual connections every internal signal is transformed by a filter and nonlinearity before being seen by subsequent layers. Repeated application of a simple expansion rule generates deep networks whose structural layouts are precisely truncated fractals. Abstract: We introduce a design strategy for neural network macro-architecture based on self-similarity.
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