https://arxiv.org/pdf/1611.08408.pdf key point use adversarial net to distinguish if input masks are from segmentation net or ground truth. this allows to add auxiliary loss, like below: where mce: multi class cross-entropy bce: binary cross entropy s(): segmentation network a(): adversarial network y: ground truth x: input s(x): segmentation network output Read more…
https://arxiv.org/abs/1912.07768 This work suggests that surrogate data need not be drawn from the original data distribution.This paper investigates the question of whether we can train a data-generating network that can produce synthetic data that effectively and efficiently teaches a target task to a learner propose new method to create synthetic Read more…
arxiv: https://arxiv.org/pdf/2102.06171v1.pdf key points introduce NF nets which combines multiple ideas to avoid using batch norm to get on-par performance but along with just using a bunch of non-BN techniques, this paper introduces adaptive gradient clipping(AGC) to make it actually train well to reach comparable results matching that of using Read more…
arxiv: https://arxiv.org/abs/1803.01534
this paper propose a network structure(“PANet”) with bottom up path augmentation and a few other tweak methods which results in better instance segmentation performance.
(more…)arxiv: https://arxiv.org/pdf/1807.06521.pdf
trying to attach attention module to CNN. but instead of blindly attaching it which would compute a 3D attention map which is computationally expensive, this work proposes to compute spatial attention and channel attention separately which achieves a similar effect with much less parameters.
(more…)arxiv: https://arxiv.org/abs/1406.4729
pools a fixed number of features from final feature map of backbone which can be fed to the dense layer afterwards, thus allowing network to work in a non-fixed input size manner.
(more…)arxiv: https://arxiv.org/abs/2003.12294
this approach will try to take global context into account by adopting transformer, and also use similar structure to reason semantic content.
(more…)arxiv: https://arxiv.org/pdf/1704.04861.pdf key points focus on optimizing for latency, small networks. use depthwise separable convolutions, to reduce computation as much as possible further reduce size of models based on width/resolution multiplier, but at the cost of accuracy depthwise separable convolution This is a combination of depthwise convonlution + pointwise convolution. Read more…
arxiv: https://arxiv.org/abs/1903.03273
arxiv link: https://arxiv.org/abs/1709.08429