Learning Types
32,670 papers (1 classified directly here)
Subtopics · a paper can sit in several, so these overlap
Representation Learning (5,307)
Multimodal Learning (3,969)
Self-Supervised Learning (3,492)
Transfer Learning (2,890)
Adversarial Learning (2,199)
Deep Learning (1,883)
Contrastive Learning (1,730)
Reinforcement Learning (1,500)
Knowledge Distillation (1,494)
Fine-Tuning (1,117)
Multi-Task Learning (1,069)
Domain Adaptation (977)
Zero-Shot Learning (758)
Unsupervised Learning (734)
Few-Shot Learning (694)
Classification (621)
Retrieval-Augmented Generation (606)
Data Augmentation (583)
Semi-Supervised Learning (450)
Weakly Supervised Learning (429)
Continual Learning (408)
In-Context Learning (387)
Sequence Modeling (375)
Meta-Learning (252)
Metric Learning (232)
Ensemble Learning (223)
Federated Learning (215)
Multi-Lingual Learning (199)
Robustness (197)
Parameter-Efficient Fine-Tuning (190)
Imitation Learning (180)
Prompt Engineering (170)
Chain-of-Thought Reasoning (153)
Curriculum Learning (146)
Synthetic Data Generation (143)
Reinforcement Learning from Human Feedback (141)
Multi-Label Classification (123)
Regression (117)
Generative Model (109)
Semantic Segmentation (106)
Domain Generalization (105)
Uncertainty Quantification (98)
Controllable Generation (93)
Text Generation (86)
Neural Architecture Search (80)
Object Detection (80)
Image Generation (79)
Multi-Agent Systems (76)
Feature Learning (74)
Preference Learning (73)
Machine Translation (71)
Out-of-Distribution Detection (68)
Machine Unlearning (63)
Generation (61)
Reasoning (54)
Multi-View Learning (53)
Time Series (52)
Instruction Tuning (50)
Distributed Learning (50)
Image Restoration (49)
Chain-of-Thought (48)
Active Learning (47)
Text Classification (43)
Distribution Shift (43)
Structured Prediction (42)
Clustering (41)
Image Classification (40)
Model Merging (37)
Sparse Learning (36)
Sequence Labeling (36)
Multi-Instance Learning (35)
Anomaly Detection (33)
Image Processing (33)
Code Generation (31)
Fairness (30)
Robust Learning (28)
Multi-Domain Learning (27)
Supervised Learning (26)
Model-Based RL (25)
Natural Language Generation (24)
Privacy (21)
Multi-Objective Optimization (19)
Multi-Scale Learning (19)
Neural Machine Translation (18)
Causal Inference (14)
Instance Segmentation (9)
Interactive Learning (9)
Multi-Label Learning (9)
Multi-Source Learning (9)
Multi-Class Classification (8)
Papers per year
1
22
25
29
26
38
46
60
109
129
179
310
686
1,294
2,286
3,018
3,254
3,930
4,067
4,819
5,737
2,605
'10
'15
'20
'25
Papers (including subtopics)
Rethinking Deep Image Prior for Denoising
ICCV 2021
Rethinking Graph Regularization for Graph Neural Networks
AAAI 2021
Rethinking Label-Wise Cross-Modal Retrieval from A Semantic Sharing Perspective
IJCAI 2021
Rethinking Neural Operations for Diverse Tasks
NeurIPS 2021
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
ICML 2021
Rethinking Text Segmentation: A Novel Dataset and a Text-Specific Refinement Approach
CVPR 2021
Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective
ICCV 2021
Rethinking the Heatmap Regression for Bottom-Up Human Pose Estimation
CVPR 2021
Rethinking Why Intermediate-Task Fine-Tuning Works
EMNLP 2021
Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent Variables
EMNLP 2021
[RETRACTED] Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training
ACL 2021
[RETRACTED] DMix: Distance Constrained Interpolative Mixup
EMNLP 2021
RETRACTED: Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance
AAAI 2021
[RETRACTED] Sequence Mixup for Zero-Shot Cross-Lingual Part-Of-Speech Tagging
EMNLP 2021
Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning
EMNLP 2021
Retrieval Augmentation Reduces Hallucination in Conversation
EMNLP 2021
Retrieval Augmented Code Generation and Summarization
EMNLP 2021
Retrospective Reader for Machine Reading Comprehension
AAAI 2021
Return-Based Contrastive Representation Learning for Reinforcement Learning
ICLR 2021
Revisiting Few-sample BERT Fine-tuning
ICLR 2021
Revisiting Iterative Back-Translation from the Perspective of Compositional Generalization
AAAI 2021
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework
CVPR 2021
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research
ICML 2021
Revisiting ResNets: Improved Training and Scaling Strategies
NeurIPS 2021
Revisiting the Negative Data of Distantly Supervised Relation Extraction
ACL 2021