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Model Architecture

4351 directly classified papers

Papers per year

Papers

Neural Finite-State Transducers: Beyond Rational Relations NAACL 2019
Segmentation-Driven 6D Object Pose Estimation CVPR 2019
MnasNet: Platform-Aware Neural Architecture Search for Mobile CVPR 2019
Circulant Binary Convolutional Networks: Enhancing the Performance of 1-Bit DCNNs With Circulant Back Propagation CVPR 2019
Efficient and Accurate Arbitrary-Shaped Text Detection With Pixel Aggregation Network ICCV 2019
Ground-to-Aerial Image Geo-Localization With a Hard Exemplar Reweighting Triplet Loss ICCV 2019
ABD-Net: Attentive but Diverse Person Re-Identification ICCV 2019
Composite Shape Modeling via Latent Space Factorization ICCV 2019
Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking ICCV 2019
Learning to Reconstruct 3D Manhattan Wireframes From a Single Image ICCV 2019
Expert Sample Consensus Applied to Camera Re-Localization ICCV 2019
Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression ICCV 2019
An Empirical Study of Spatial Attention Mechanisms in Deep Networks ICCV 2019
XNAS: Neural Architecture Search with Expert Advice NIPS 2019
Convolution with even-sized kernels and symmetric padding NIPS 2019
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness NIPS 2019
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks NIPS 2019
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks NIPS 2019
Post training 4-bit quantization of convolutional networks for rapid-deployment NIPS 2019
Gaussian-Based Pooling for Convolutional Neural Networks NIPS 2019
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks NIPS 2019
STAR-Caps: Capsule Networks with Straight-Through Attentive Routing NIPS 2019
Certifying Geometric Robustness of Neural Networks NIPS 2019
Constrained deep neural network architecture search for IoT devices accounting for hardware calibration NIPS 2019
A Benchmark for Interpretability Methods in Deep Neural Networks NIPS 2019