Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources.
In this paper, we reduce this cost by exploiting the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and suppression (FBS), a new method to predictively amplify salient convolutional channels and skip unimportant ones at run-time.
FBS introduces small auxiliary connections to existing convolutional layers.
In contrast to channel pruning methods which permanently remove channels, it preserves the full network structures and accelerates convolution by dynamically skipping unimportant input and output channels.
FBS-augmented networks are trained with conventional stochastic gradient descent, making it readily available for many state-of-the-art CNNs.
We compare FBS to a range of existing channel pruning and dynamic execution schemes and demonstrate large improvements on ImageNet classification.
Experiments show that FBS can respectively provide 5× and 2× savings in compute on VGG-16 and ResNet-18, both with less than 0.6% top-5 accuracy loss.
State-of-the-art vision and image-based tasks such as image classification BID19 BID33 BID11 , object detection BID31 and segmentation BID26 are all built upon deep convolutional neural networks (CNNs).
While CNN architectures have evolved to become more efficient, the general trend has been to use larger models with greater memory utilization, bandwidth and compute requirements to achieve higher accuracy.
The formidable amount of computational resources used by CNNs present a great challenge in the deployment of CNNs in both cost-sensitive cloud services and low-powered edge computing applications.One common approach to reduce the memory, bandwidth and compute costs is to prune over-parameterized CNNs.
If performed in a coarse-grain manner this approach is known as channel pruning BID37 BID25 BID35 .
Channel pruning evaluates channel saliency measures and removes all input and output connections from unimportant channels-generating a smaller dense model.
A saliency-based pruning method, however, has threefold disadvantages.
Firstly, by removing channels, the capabilities of CNNs are permanently lost, and the resulting CNN may never regain its accuracy for difficult inputs for which the removed channels were responsible.
Secondly, despite the fact that channel pruning may drastically shrink model size, without careful design, computational resources cannot be effectively reduced in a CNN without a detrimental impact on its accuracy.
Finally, the saliency of a neuron is not static, which can be illustrated by the feature visualization in FIG0 .
Here, a CNN is shown a set of input images, certain channel neurons in a convolutional output may get highly excited, whereas another set of images elicit little response from the same channels.
This is in line with our understanding of CNNs that neurons in a convolutional layer specialize in recognizing distinct features, and the relative importance of a neuron depends heavily on the inputs.The above shortcomings prompt the question: why should we prune by static importance, if the importance is highly input-dependent?
Surely, a more promising alternative is to prune dynamically depending on the current input.
A dynamic channel pruning strategy allows the network to learn to prioritize certain convolutional channels and ignore irrelevant ones.
Instead of simply reducing model size at the cost of accuracy with pruning, we can accelerate convolution by selectively computing only a subset of channels predicted to be important at run-time, while considering the sparse input from the preceding convolution layer.
In effect, the amount of cached activations and the number of read, write and arithmetic operations used by a well-designed dynamic model can be almost identical to an equivalently sparse statically pruned one.
In addition to saving computational resources, a dynamic model preserves all neurons of the full model, which minimizes the impact on task accuracy.In this paper, we propose feature boosting and suppression (FBS) to dynamically amplify and suppress output channels computed by the convolutional layer.
Intuitively, we can imagine that the flow of information of each output channel can be amplified or restricted under the control of a "valve".
This allows salient information to flow freely while we stop all information from unimportant channels and skip their computation.
Unlike pruning statically, the valves use features from the previous layer to predict the saliency of output channels.
With conventional stochastic gradient descent (SGD) methods, the predictor can learn to adapt itself by observing the input and output features of the convolution operation.FBS introduces tiny auxiliary connections to existing convolutional layers.
The minimal overhead added to the existing model is thus negligible when compared to the potential speed up provided by the dynamic sparsity.
Existing dynamic computation strategies in CNNs BID28 BID2 produce on/off pruning decisions or execution path selections.
Training them thus often resorts to reinforcement learning, which in practice is often computationally expensive.
Even though FBS similarly use non-differentiable functions, contrary to these methods, the unified losses are still wellminimized with conventional SGD.We apply FBS to a custom CIFAR-10 ( BID20 classifier and popular CNN models such as VGG-16 BID33 and ResNet-18 (He et al., 2016) trained on the ImageNet dataset BID3 .
Empirical results show that under the same speed-ups, FBS can produce models with validation accuracies surpassing all other channel pruning and dynamic conditional execution methods examined in the paper.
BID11 , the outputs from certain channel neurons may vary drastically.
The top rows in
(a) and
(b) are found respectively to greatly excite neurons in channels 114 and 181 of layer block 3b/conv2, whereas the bottom images elicit little activation from the same channel neurons.
The number below each image indicate the maximum values observed in the channel before adding the shortcut and activation.
Finally,
(c) shows the distribution of maximum activations observed in the first 20 channels.
In summary, we proposed feature boosting and suppression that helps CNNs to achieve significant reductions in the compute required while maintaining high accuracies.
FBS fully preserves the capabilities of CNNs and predictively boosts important channels to help the accelerated models retain high accuracies.
We demonstrated that FBS achieves around 2× and 5× savings in computation respectively on ResNet-18 and VGG-16 within 0.6% loss of top-5 accuracy.
Under the same performance constraints, the accuracy gained by FBS surpasses all recent structured pruning and dynamic execution methods examined in this paper.
In addition, it can serve as an off-the-shelf technique for accelerating many popular CNN networks and the fine-tuning process is unified in the traditional SGD which requires no algorithmic changes in training.
Finally, the implementation of FBS and the optimized networks are fully open source and released to the public 1 .
We make convolutional layers run faster by dynamically boosting and suppressing channels in feature computation.