Improving image classifiers for small datasets by learning rate adaptations
Our paper introduces an efficient combination of established techniques to improve classifier performance, in terms of accuracy and training time. We achieve two-fold to ten-fold speedup in nearing state of the art accuracy, over different model architectures, by dynamically tuning the learning rate. We find it especially beneficial in the case of a small dataset, where reliability of machine reasoning is lower. We validate our approach by comparing our method versus vanilla training on CIFAR-10. We also demonstrate its practical viability by implementing on an unbalanced corpus of diagnostic images.
Code (0)
등록된 구현이 없습니다.
Tasks
DiagnosticSimilar Papers 제목 키워드 기반
Class-Incremental Learning with Strong Pre-trained Models
Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes). Instead, we explore an understudied real-world setting of CIL that starts with a stron…
class-incremental learningClass Incremental LearningIncremental LearningInducing Group Fairness in Prompt-Based Language Model Decisions
Classifiers are used throughout industry to enforce policies, ranging from the detection of toxic content to age-appropriate content filtering. While these classifiers serve important functions, it is also essential that…
FairnessLanguage ModelingLanguage ModellingDeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers
We introduce DeepCert, a tool-supported method for verifying the robustness of deep neural network (DNN) image classifiers to contextually relevant perturbations such as blur, haze, and changes in image contrast. While t…
Training CNN Classifiers for Semantic Segmentation using Partially Annotated Images: with Application on Human Thigh and Calf MRI
Objective: Medical image datasets with pixel-level labels tend to have a limited number of organ or tissue label classes annotated, even when the images have wide anatomical coverage. With supervised learning, multiple c…
Semantic SegmentationCombining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection
Visual artefacts of early diabetic retinopathy in retinal fundus images are usually small in size, inconspicuous, and scattered all over retina. Detecting diabetic retinopathy requires physicians to look at the whole ima…
Diabetic Retinopathy Detection