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← Optimization & Theory
Deep Learning
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Optimization & Theory
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Generalization
17 directly classified papers
Papers per year
2019: 1
2020: 4
2021: 1
2022: 4
2023: 2
2024: 3
2025: 2
Papers
Generalization of Graph Neural Networks Is Robust to Model Mismatch
AAAI 2025
FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing
CVPR 2025
Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection
CVPR 2024
Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data
NIPS 2024
An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem
NIPS 2024
AGAIN: Adversarial Training With Attribution Span Enlargement and Hybrid Feature Fusion
CVPR 2023
Networks are Slacking Off: Understanding Generalization Problem in Image Deraining
NIPS 2023
Reflash Dropout in Image Super-Resolution
CVPR 2022
Exploiting Explainable Metrics for Augmented SGD
CVPR 2022
Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective
NIPS 2022
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions With Superior OOD Generalization
CVPR 2022
Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks
AAAI 2021
In search of robust measures of generalization
NIPS 2020
Computing the Testing Error Without a Testing Set
CVPR 2020
Understanding Generalization in Neural Networks for Robustness against Adversarial Vulnerabilities
AAAI 2020
High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks
CVPR 2020
Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization
EMNLP 2019
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