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Found 12 papers, 4 papers with code
Cycle Self-Training for Semi-Supervised Object Detection with Distribution Consistency Reweighting
To address the coupling problem, we propose a Cycle Self-Training (CST) framework for SSOD, which consists of two teachers T1 and T2, two students S1 and S2.
MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones
Instead of performing stylization frame by frame, only key frames in the original video are processed by a pre-trained deep neural network (DNN) on edge servers, while the rest of stylized intermediate frames are generated by our designed optical-flow-based frame interpolation algorithm on mobile phones.
Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness
In this work, we derive a new regularized risk, in which the regularizer can adaptively encourage the accuracy and robustness of the smoothed counterpart when training the base classifier.
Conditional Transferring Features: Scaling GANs to Thousands of Classes with 30% Less High-quality Data for Training
As such, training our GAN architecture requires much fewer high-quality images with a small number of additional low-quality images.
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack
We intuitively and empirically prove the rationality of our method in reducing the search space.
SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning
It becomes an open question whether escaping sharp minima can improve the generalization.
MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks
Our experiments show that different adversarial strengths, i. e., perturbation levels of adversarial examples, have different working zones to resist the attack.
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
We mathematically prove the convergence of TernGrad under the assumption of a bound on gradients.
Coordinating Filters for Faster Deep Neural Networks
Moreover, Force Regularization better initializes the low-rank DNNs such that the fine-tuning can converge faster toward higher accuracy.
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation
Our DNN has 4. 1M parameters, which is only 6. 7% of AlexNet or 59% of GoogLeNet.
Learning Structured Sparsity in Deep Neural Networks
SSL can: (1) learn a compact structure from a bigger DNN to reduce computation cost; (2) obtain a hardware-friendly structured sparsity of DNN to efficiently accelerate the DNNs evaluation.
A New Learning Method for Inference Accuracy, Core Occupation, and Performance Co-optimization on TrueNorth Chip
IBM TrueNorth chip uses digital spikes to perform neuromorphic computing and achieves ultrahigh execution parallelism and power efficiency.
