11 papers with code • 3 benchmarks • 1 datasets
MVTecAD
Subtasks
Most implemented papers
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
charlesq34/pointnet2
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NeurIPS 2017
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Anomaly Detection via Reverse Distillation from One-Class Embedding
Knowledge distillation (KD) achieves promising results on the challenging problem of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher-student (T-S) model provides essential evidence for AD.
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
autonomousvision/transfuser
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CVPR 2021
How should representations from complementary sensors be integrated for autonomous driving?
MTet: Multi-domain Translation for English and Vietnamese
vietai/mTet
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We introduce MTet, the largest publicly available parallel corpus for English-Vietnamese translation.
A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
ray075hl/Bi-Model-Intent-And-Slot
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NAACL 2018
The most effective algorithms are based on the structures of sequence to sequence models (or "encoder-decoder" models), and generate the intents and semantic tags either using separate models or a joint model.
Compositional Learning of Image-Text Query for Image Retrieval
In this paper, we investigate the problem of retrieving images from a database based on a multi-modal (image-text) query.
An efficient encoder-decoder architecture with top-down attention for speech separation
JusperLee/TDANet
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In addition, a large-size version of TDANet obtained SOTA results on three datasets, with MACs still only 10\% of Sepformer and the CPU inference time only 24\% of Sepformer.
Gradient Gating for Deep Multi-Rate Learning on Graphs
We present Gradient Gating (G$^2$), a novel framework for improving the performance of Graph Neural Networks (GNNs).
Dual Cross-Attention for Medical Image Segmentation
gorkemcanates/dual-cross-attention
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DCA addresses the semantic gap between encoder and decoder features by sequentially capturing channel and spatial dependencies across multi-scale encoder features.
