In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Ex Input:
Machine learned large-scale retrieval systems require a large amount of training data representing query-item relevance. However, collecting users' explicit feedback is costly. In this paper, we propose to leverage user logs and implicit feedback as auxiliary objectives to improve relevance modeling in retrieval systems. Specifically, we adopt a two-tower neural net architecture to model query-item relevance given both collaborative and content information. By introducing auxiliary tasks trained with much richer implicit user feedback data, we improve the quality and resolution for the learned representations of queries and items. Applying these learned representations to an industrial retrieval system has delivered significant improvements.

Ex Output:
We propose a novel two-tower shared-bottom model architecture for transferring knowledge from rich implicit feedbacks to predict relevance for large-scale retrieval systems.


Ex Input:
Now GANs can generate more and more realistic face images that can easily fool human beings.   In contrast, a common convolutional neural network(CNN), e.g. ResNet-18, can achieve more than 99.9% accuracy in discerning fake/real faces if training and testing faces are from the same source. In this paper, we performed both human studies and CNN experiments, which led us to two important findings. One finding is that the textures of fake faces are substantially different from real ones. CNNs can capture local image texture information for recognizing fake/real face, while such cues are easily overlooked by humans. The other finding is that global image texture information is more robust to image editing and generalizable to fake faces from different GANs and datasets. Based on the above findings, we propose  a  novel  architecture  coined  as  Gram-Net,  which  incorporates  “Gram Block” in multiple semantic levels to extract global image texture representations. Experimental results demonstrate that our Gram-Net performs better than existing approaches for fake face detection.   Especially, our Gram-Net is more robust to image editing, e.g.  downsampling, JPEG compression, blur, and noise.   More importantly, our Gram-Net generalizes significantly better in detecting fake faces from GAN models not seen in the training phase.

Ex Output:
An empirical study on fake images reveals that texture is an important cue that current fake images differ from real images. Our improved model capturing global texture statistics shows better cross-GAN fake image detection performance.


Ex Input:
It is common practice to decay the learning rate. Here we show one can usually obtain the same learning curve on both training and test sets by instead increasing the batch size during training. This procedure is successful for stochastic gradient descent (SGD), SGD with momentum, Nesterov momentum, and Adam. It reaches equivalent test accuracies after the same number of training epochs, but with fewer parameter updates, leading to greater parallelism and shorter training times. We can further reduce the number of parameter updates by increasing the learning rate $\epsilon$ and scaling the batch size $B \propto \epsilon$. Finally, one can increase the momentum coefficient $m$ and scale $B \propto 1/(1-m)$, although this tends to slightly reduce the test accuracy. Crucially, our techniques allow us to repurpose existing training schedules for large batch training with no hyper-parameter tuning. We train ResNet-50 on ImageNet to 76.1% validation accuracy in under 30 minutes.

Ex Output:

Decaying the learning rate and increasing the batch size during training are equivalent.