Papers Small Data Image Classification
“Small Data Image Classification” 태그가 달린 논문 179편 · 필터 해제
Generalizable prediction of academic performance from short texts on social media
It has already been established that digital traces can be used to predict various human attributes. In most cases, however, predictive models rely on features that are specific to a particular source of digital trace da…
Small Data Image ClassificationInteractive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation
For many NLP applications, such as question answering and summarisation, the goal is to select the best solution from a large space of candidates to meet a particular user's needs. To address the lack of user-specific tr…
Bayesian OptimisationCommunity Question AnsweringQuestion AnsweringReinforcement Learning+1Estimating uncertainty of earthquake rupture using Bayesian neural network
Bayesian neural networks (BNN) are the probabilistic model that combines the strengths of both neural network (NN) and stochastic processes. As a result, BNN can combat overfitting and perform well in applications where …
FrictionSmall Data Image ClassificationLearning to Control Latent Representations for Few-Shot Learning of Named Entities
Humans excel in continuously learning with small data without forgetting how to solve old problems. However, neural networks require large datasets to compute latent representations across different tasks while minimizin…
Few-Shot LearningManagementNatural Language UnderstandingRepresentation Learning+1CD2 : Combined Distances of Contrast Distributions for the Assessment of Perceptual Quality of Image Processing
The quality of visual input is very important for both human and machine perception. Consequently many processing techniques exist that deal with different distortions. Usually image processing is applied freely and lack…
Image Quality AssessmentSmall Data Image ClassificationComparison of Deep learning models on time series forecasting : a case study of Dissolved Oxygen Prediction
Deep learning has achieved impressive prediction performance in the field of sequence learning recently. Dissolved oxygen prediction, as a kind of time-series forecasting, is suitable for this technique. Although many re…
Deep LearningSmall Data Image ClassificationTime SeriesTime Series Analysis+1Transfer Learning of fMRI Dynamics
As a mental disorder progresses, it may affect brain structure, but brain function expressed in brain dynamics is affected much earlier. Capturing the moment when brain dynamics express the disorder is crucial for early …
DiagnosticSmall Data Image ClassificationTransfer LearningSMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery
In drug-discovery-related tasks such as virtual screening, machine learning is emerging as a promising way to predict molecular properties. Conventionally, molecular fingerprints (numerical representations of molecules) …
Drug DiscoveryLanguage ModelingLanguage ModellingSmall Data Image Classification+1Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds
Automatic question generation aims at the generation of questions from a context, with the corresponding answers being sub-spans of the given passage. Whereas, most of the methods mostly rely on heuristic rules to genera…
DecoderLanguage ModellingQuestion AnsweringQuestion Generation+2Big Generalizations with Small Data: Exploring the Role of Training Samples in Learning Adjectives of Size
In this paper, we experiment with a recently proposed visual reasoning task dealing with quantities {--} modeling the multimodal, contextually-dependent meaning of size adjectives ({`}big{'}, {`}small{'}) {--} and explor…
Small Data Image ClassificationVisual ReasoningInteractive Evidence Detection: train state-of-the-art model out-of-domain or simple model interactively?
Finding evidence is of vital importance in research as well as fact checking and an evidence detection method would be useful in speeding up this process. However, when addressing a new topic there is no training data an…
Fact CheckingGPUmodelSmall Data Image ClassificationSampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset
This paper tackles the challenge presented by small-data to the task of Bayesian inference. A novel methodology, based on manifold learning and manifold sampling, is proposed for solving this computational statistics pro…
Bayesian InferenceSmall Data Image ClassificationPromoting the Knowledge of Source Syntax in Transformer NMT Is Not Needed
The utility of linguistic annotation in neural machine translation seemed to had been established in past papers. The experiments were however limited to recurrent sequence-to-sequence architectures and relatively small …
Machine TranslationMulti-Task LearningNMTSmall Data Image Classification+1Semantic Segmentation of Skin Lesions using a Small Data Set
Early detection of melanoma is difficult for the human eye but a crucial step towards reducing its death rate. Computerized detection of these melanoma and other skin lesions is necessary. The central research question i…
Lesion SegmentationSemantic SegmentationSkin Lesion SegmentationSmall Data Image Classification+1Multiphase flow prediction with deep neural networks
This paper proposes a deep neural network approach for predicting multiphase flow in heterogeneous domains with high computational efficiency. The deep neural network model is able to handle permeability heterogeneity in…
Computational EfficiencyPredictionSmall Data Image ClassificationTransfer Learning+1Fragment Graphical Variational AutoEncoding for Screening Molecules with Small Data
In the majority of molecular optimization tasks, predictive machine learning (ML) models are limited due to the unavailability and cost of generating big experimental datasets on the specific task. To circumvent this lim…
Small Data Image ClassificationGenerative Modeling for Small-Data Object Detection
This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense. This is a common challenge today with machin…
Objectobject-detectionObject DetectionPedestrian Detection+2Deep Kernels with Probabilistic Embeddings for Small-Data Learning
Gaussian Processes (GPs) are known to provide accurate predictions and uncertainty estimates even with small amounts of labeled data by capturing similarity between data points through their kernel function. However trad…
Gaussian ProcessesRepresentation LearningSmall Data Image ClassificationSpoofing and Anti-Spoofing with Wax Figure Faces
We have witnessed rapid advances in both face presentation attack models and presentation attack detection (PAD) in recent years. Compared to widely studied 2D face presentation attacks (e.g. printed photos and video rep…
Face DetectionFace RecognitionSmall Data Image ClassificationAccelerometer-Based Gait Segmentation: Simultaneously User and Adversary Identification
In this paper, we introduce a new gait segmentation method based on accelerometer data and develop a new distance function between two time series, showing novel and effectiveness in simultaneously identifying user and a…
Binary ClassificationGeneral ClassificationSmall Data Image ClassificationTime Series+1