Browse SoTA > Methodology > Incremental Learning

Incremental Learning

82 papers with code · Methodology

Benchmarks

Greatest papers with code

Expected Similarity Estimation for Large-Scale Batch and Streaming Anomaly Detection

25 Jan 2016numenta/NAB

We present a novel algorithm for anomaly detection on very large datasets and data streams.

ANOMALY DETECTION INCREMENTAL LEARNING

Distractor-aware Siamese Networks for Visual Object Tracking

ECCV 2018 foolwood/DaSiamRPN

During the off-line training phase, an effective sampling strategy is introduced to control this distribution and make the model focus on the semantic distractors.

INCREMENTAL LEARNING VISUAL OBJECT TRACKING VISUAL TRACKING

Three scenarios for continual learning

15 Apr 2019GMvandeVen/continual-learning

Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning difficult for machine learning.

CONTINUAL LEARNING INCREMENTAL LEARNING

Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns

CVPR 2018 ahangchen/TFusion

Most of the proposed person re-identification algorithms conduct supervised training and testing on single labeled datasets with small size, so directly deploying these trained models to a large-scale real-world camera network may lead to poor performance due to underfitting.

INCREMENTAL LEARNING LEARNING-TO-RANK TRANSFER LEARNING UNSUPERVISED PERSON RE-IDENTIFICATION

A cognitive neural architecture able to learn and communicate through natural language

10 Jun 2015golosio/annabell

Communicative interactions involve a kind of procedural knowledge that is used by the human brain for processing verbal and nonverbal inputs and for language production.

INCREMENTAL LEARNING

A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

NeurIPS 2018 pokaxpoka/deep_Mahalanobis_detector

Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning applications.

INCREMENTAL LEARNING

Meta-Aggregating Networks for Class-Incremental Learning

ICLR 2021 yaoyao-liu/mnemonics

Class-Incremental Learning (CIL) aims to learn a classification model with the number of classes increasing phase-by-phase.

INCREMENTAL LEARNING

Mnemonics Training: Multi-Class Incremental Learning without Forgetting

CVPR 2020 yaoyao-liu/mnemonics

However, there is an inherent trade-off to effectively learning new concepts without catastrophic forgetting of previous ones.

INCREMENTAL LEARNING

iCaRL: Incremental Classifier and Representation Learning

CVPR 2017 yaoyao-liu/mnemonics

A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data.

INCREMENTAL LEARNING REPRESENTATION LEARNING

FoCL: Feature-Oriented Continual Learning for Generative Models

9 Mar 2020nvcuong/variational-continual-learning

In this paper, we propose a general framework in continual learning for generative models: Feature-oriented Continual Learning (FoCL).

CONTINUAL LEARNING INCREMENTAL LEARNING