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Found 15 papers, 8 papers with code
Unraveling the Mystery of Artifacts in Machine Generated Text
As neural Text Generation Models (TGM) have become more and more capable of generating text indistinguishable from human-written ones, the misuse of text generation technologies can have serious ramifications.
Label Matching Semi-Supervised Object Detection
To remedy this issue, we present a novel label assignment mechanism for self-training framework, namely proposal self-assignment, which injects the proposals from student into teacher and generates accurate pseudo labels to match each proposal in the student model accordingly.
Slimmable Domain Adaptation
In this paper, we introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank, from which models of different capacities can be sampled to accommodate different accuracy-efficiency trade-offs.
2nd Place Solution for ICCV 2021 VIPriors Image Classification Challenge: An Attract-and-Repulse Learning Approach
Therefore, it is crucial to study how to learn more discriminative representations while avoiding over-fitting.
Transductive CLIP with Class-Conditional Contrastive Learning
This framework can reduce the impact of noisy labels from CLIP model effectively by combining both techniques.
Learning Domain Adaptive Object Detection with Probabilistic Teacher
In addition, we conduct anchor adaptation in parallel with localization adaptation, since anchor can be regarded as a learnable parameter.
Dynamic Domain Generalization
Specifically, we leverage a meta-adjuster to twist the network parameters based on the static model with respect to different data from different domains.
Probing Simile Knowledge from Pre-trained Language Models
In this paper, we probe simile knowledge from PLMs to solve the SI and SG tasks in the unified framework of simile triple completion for the first time.
Semi-Supervised Domain Generalization in Real World: New Benchmark and Strong Baseline
In this paper, we introduce a novel task, termed as semi-supervised domain generalization, to study how to interact the labeled and unlabeled domains, and establish two benchmarks including a web-crawled dataset, which poses a novel yet realistic challenge to push the limits of existing technologies.
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning
A general theoretical analysis shows that the solutions generated from a sequence of the constrained optimizations converge to the optimal solution of the LP if the error is controlled properly.
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation
Usually, the given source domain pre-trained model is expected to optimize with only unlabeled target data, which is termed as source-free unsupervised domain adaptation.
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection
Therefore, it yields our attention to suppress false positive in each target domain in an unsupervised way.
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data
However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain.
DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
Materials 3, 023804] and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training.
Computational Physics
Fast and Robust Rank Aggregation against Model Misspecification
In rank aggregation, preferences from different users are summarized into a total order under the homogeneous data assumption.
