91,849 research outputs found
FiLM: Visual Reasoning with a General Conditioning Layer
We introduce a general-purpose conditioning method for neural networks called
FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network
computation via a simple, feature-wise affine transformation based on
conditioning information. We show that FiLM layers are highly effective for
visual reasoning - answering image-related questions which require a
multi-step, high-level process - a task which has proven difficult for standard
deep learning methods that do not explicitly model reasoning. Specifically, we
show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error
for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are
robust to ablations and architectural modifications, and 4) generalize well to
challenging, new data from few examples or even zero-shot.Comment: AAAI 2018. Code available at http://github.com/ethanjperez/film .
Extends arXiv:1707.0301
Residual Attention Network for Image Classification
In this work, we propose "Residual Attention Network", a convolutional neural
network using attention mechanism which can incorporate with state-of-art feed
forward network architecture in an end-to-end training fashion. Our Residual
Attention Network is built by stacking Attention Modules which generate
attention-aware features. The attention-aware features from different modules
change adaptively as layers going deeper. Inside each Attention Module,
bottom-up top-down feedforward structure is used to unfold the feedforward and
feedback attention process into a single feedforward process. Importantly, we
propose attention residual learning to train very deep Residual Attention
Networks which can be easily scaled up to hundreds of layers. Extensive
analyses are conducted on CIFAR-10 and CIFAR-100 datasets to verify the
effectiveness of every module mentioned above. Our Residual Attention Network
achieves state-of-the-art object recognition performance on three benchmark
datasets including CIFAR-10 (3.90% error), CIFAR-100 (20.45% error) and
ImageNet (4.8% single model and single crop, top-5 error). Note that, our
method achieves 0.6% top-1 accuracy improvement with 46% trunk depth and 69%
forward FLOPs comparing to ResNet-200. The experiment also demonstrates that
our network is robust against noisy labels.Comment: accepted to CVPR201
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