In this paper we propose a Fair Adversarial Instance Re-weighting (FAIR) method, which uses adversarial training to learn instance weighting function that ensures fair predictions. Merging the two paradigms, it inherits desirable properties from both interpretability of reweighting and end-to-end ...
Instance re-weighting (conditional distributions are same while marginal distributions are different) : KMM 2 : Feature-based transfer learning Explicit distance: case 1 : marginal distributions are same while conditional distributions are different: TCA(MMD based) ; DAN(MK-MMD based) case 1 : cond...
Instance weighting that indicates how much this instance type contributes to the total capacity of a game server group. int hashCode() void marshall(ProtocolMarshaller protocolMarshaller) Marshalls this structured data using the given ProtocolMarshaller. void setInstanceType(String instanceType) An ...
Amazon EC2 key pairs and Amazon EC2 instances Amazon EC2 instances connect securely via key pairs, private keys decrypt Windows passwords, store keys safely August 1, 2024 Next topic:Data persistence Previous topic:EBS volume limits Need help? Try AWS re:Post Connect with an AWS IQ expert ...
train.py: Outer code for training and test for MI-AOD, including generating PASCAL VOC datasets for active learning, loading image sets and models, Instance Uncertainty Re-weighting and Informative Image Selection in general, which can be called byscript.sh. ...
Thirdly, in order to avoid the influence of the source-biased features, we propose a task re-weighting mechanism to dynamically add trade-off weights for the task-specific loss functions. Experimental results on three datasets indicate that our proposed...
Instance segmentation expects directories of images for training or validation and annotation files in COCO format. See the Data Annotation Format page for more information about the data format for instance segmentation. Image data and the corresponding annotation file is then converted to TFRecords ...
Furthermore, we introduce supervised contrastive learning to enhance the discriminative capability for fine-grained classification and a curriculum-based dynamic weighting module to adaptively balance the hierarchical feature during training. Extensive experiments on our large-scale cytology cervical cancer (...
This is also of ample relevance to our use case, since production planning for agile and responsive settings, such as in an RAS, requires that planning is (re)defined as promptly and efficiently as possible whenever needed. As a result, minimizing the allocated computational budget (without ...
Re-weighting strategies [8,9,10,11] assign different weights to different classes of training samples to solve the problem that head-class features dominate long-tail datasets. Some researchers use two-stage or multi-stage methods [12,13] to solve the problem of long-tail datasets. Generally,...