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Found 6 papers, 3 papers with code
Contrastive Regularization for Semi-Supervised Learning
Consistency regularization on label predictions becomes a fundamental technique in semi-supervised learning, but it still requires a large number of training iterations for high performance.
Regularizing Attention Networks for Anomaly Detection in Visual Question Answering
The results imply that cross-modal attention in VQA is important to improve not only VQA accuracy, but also the robustness to various anomalies.
Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks
Soft labeling becomes a common output regularization for generalization and model compression of deep neural networks.
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
TGNet learns an autoregressive model, conditioned on temporal contexts of forecasting targets from temporal-guided embedding.
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
In object detection, reducing computational cost is as important as improving accuracy for most practical usages.
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
This paper presents how we can achieve the state-of-the-art accuracy in multi-category object detection task while minimizing the computational cost by adapting and combining recent technical innovations.
