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Found 10 papers, 4 papers with code
Bayesian Optimization Meets Self-Distillation
On the other hand, Self-Distillation (SD) only transfers partial knowledge learned by the task model itself.
Is Cross-modal Information Retrieval Possible without Training?
Embeddings for a particular modality of data occupy a high-dimensional space of its own, but it can be semantically aligned to another by a simple mapping without training a deep neural net.
D2CSE: Difference-aware Deep continuous prompts for Contrastive Sentence Embeddings
This paper describes Difference-aware Deep continuous prompt for Contrastive Sentence Embeddings (D2CSE) that learns sentence embeddings.
Improving Multi-fidelity Optimization with a Recurring Learning Rate for Hyperparameter Tuning
However, it often results in selecting a sub-optimal configuration as training with the high-performing configuration typically converges slowly in an early phase.
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization
We build and fine-tune an abstractive dialogue summarization model on a shared encoder-decoder architecture using the enhanced BERT.
Abstractive Dialogue Summarization
Abstractive Text Summarization
+5
KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding
A Lite BERT (ALBERT) has been introduced to scale up deep bidirectional representation learning for natural languages.
Reducing Domain Gap by Reducing Style Bias
Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift.
Ranked #62 on
Domain Generalization
on PACS
SRM: A Style-Based Recalibration Module for Convolutional Neural Networks
Following the advance of style transfer with Convolutional Neural Networks (CNNs), the role of styles in CNNs has drawn growing attention from a broader perspective.
SRM : A Style-based Recalibration Module for Convolutional Neural Networks
Following the advance of style transfer with Convolutional Neural Networks (CNNs), the role of styles in CNNs has drawn growing attention from a broader perspective.
Ranked #129 on
Image Classification
on CIFAR-10
Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders
StarCraft, one of the most popular real-time strategy games, is a compelling environment for artificial intelligence research for both micro-level unit control and macro-level strategic decision making.
