Deep learning · generative models and autoencoders papers, 2023–2026

Key findings

• Deep learning · generative models and autoencoders went from 1,216 papers in 2023 to 1,430 in 2026, roughly 1.2x in three years.
• NeurIPS publishes the most Deep learning · generative models and autoencoders work: 1,714 papers from 2023 to 2026, 40% of the four-venue total.
• The fastest-growing overlap is with Image Generation: papers tagged with both rose from 35 in 2023 to 150 in 2026.
• Among the 11 topics under Deep Learning, it ranks #2 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 1.2x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Critical $\beta$-Scale for Posterior Collapse in Dirichlet $\beta$-VAEs
• ICML 2026(1D) Ordered Tokens Enable Efficient Test-Time Search
• ICLR 2026``Noisier'’ Noise Contrastive Estimation is (Almost) Maximum Likelihood
• CVPR 2026Anchoring and Rescaling Attention for Semantically Coherent Inbetweening
• NeurIPS 2025Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness
• ICML 2025A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models
• ICLR 2025A Watermark for Order-Agnostic Language Models
• CVPR 2025DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers
• NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
• ICML 2024A Fixed-Point Approach for Causal Generative Modeling
• ICLR 2024$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence
• CVPR 2024Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation
• NeurIPS 2023(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
• ICML 2023A Kernelized Stein Discrepancy for Biological Sequences
• ICLR 2023Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing
• CVPR 20233D GAN Inversion With Facial Symmetry Prior
• NeurIPS 2026Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization
• ICML 2026$f$-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data
• ICLR 2026Analyzing and Evaluating Unbiased Language Model Watermark
• CVPR 2026Bidirectional Normalizing Flow From Data to Noise and Back

All Deep learning · generative models and autoencoders papers

1,216 papers, page 3 of 25

101. ICLR 2023Distributed Extra-gradient with Optimal Complexity and Communication Guarantees
102. ICLR 2023Explicitly Minimizing the Blur Error of Variational Autoencoders
103. ICLR 2023Fast Sampling of Diffusion Models with Exponential Integrator
104. ICLR 2023Finding the Global Semantic Representation in GAN through Fréchet Mean
105. ICLR 2023Flow Matching for Generative Modeling
106. ICLR 2023Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
107. ICLR 2023Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions
108. ICLR 2023Generative Augmented Flow Networks
109. ICLR 2023Generative Modeling Helps Weak Supervision (and Vice Versa)
110. ICLR 2023Generative Modelling with Inverse Heat Dissipation
111. ICLR 2023Geometrically regularized autoencoders for non-Euclidean data
112. ICLR 2023GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
113. ICLR 2023GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure
114. ICLR 2023Gromov-Wasserstein Autoencoders
115. ICLR 2023Guiding Energy-based Models via Contrastive Latent Variables
116. ICLR 2023Hierarchical Sliced Wasserstein Distance
117. ICLR 2023ImaginaryNet: Learning Object Detectors without Real Images and Annotations
118. ICLR 2023Imitating Human Behaviour with Diffusion Models
119. ICLR 2023InCoder: A Generative Model for Code Infilling and Synthesis
120. ICLR 2023Incremental Learning of Structured Memory via Closed-Loop Transcription
121. ICLR 2023Learning Diffusion Bridges on Constrained Domains
122. ICLR 2023MeshDiffusion: Score-based Generative 3D Mesh Modeling
123. ICLR 2023MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises
124. ICLR 2023Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve
125. ICLR 2023Multifactor Sequential Disentanglement via Structured Koopman Autoencoders
126. ICLR 2023Neural Implicit Shape Editing using Boundary Sensitivity
127. ICLR 2023Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity
128. ICLR 2023Novel View Synthesis with Diffusion Models
129. ICLR 2023On Accelerated Perceptrons and Beyond
130. ICLR 2023OPTQ: Accurate Quantization for Generative Pre-trained Transformers
131. ICLR 2023Perfectly Secure Steganography Using Minimum Entropy Coupling
132. ICLR 2023Pitfalls of Gaussians as a noise distribution in NCE
133. ICLR 2023Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models
134. ICLR 2023Quantized Compressed Sensing with Score-Based Generative Models
135. ICLR 2023Revisiting adapters with adversarial training
136. ICLR 2023Robust and Controllable Object-Centric Learning through Energy-based Models
137. ICLR 2023Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
138. ICLR 2023Semi-supervised learning with a principled likelihood from a generative model of data curation
139. ICLR 2023Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective
140. ICLR 2023Stable Target Field for Reduced Variance Score Estimation in Diffusion Models
141. ICLR 2023STaSy: Score-based Tabular data Synthesis
142. ICLR 2023Structure by Architecture: Structured Representations without Regularization
143. ICLR 2023Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees
144. ICLR 2023TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
145. ICLR 2023The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation
146. ICLR 2023The Role of ImageNet Classes in Fréchet Inception Distance
147. ICLR 2023Trading Information between Latents in Hierarchical Variational Autoencoders
148. ICLR 2023Transformers are Sample-Efficient World Models
149. ICLR 2023Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders
150. ICLR 2023Verifying the Union of Manifolds Hypothesis for Image Data

FAQ

How many Deep learning · generative models and autoencoders papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

4,314 in total: 1,216 in 2023, 768 in 2024, 900 in 2025, 1,430 in 2026.

How many Deep learning · generative models and autoencoders papers are at NeurIPS 2026?

NeurIPS 2026 has 349 Deep learning · generative models and autoencoders papers, versus 240 the year before.

How many Deep learning · generative models and autoencoders papers are at ICLR 2026?

ICLR 2026 has 303 Deep learning · generative models and autoencoders papers, versus 275 the year before.

How many Deep learning · generative models and autoencoders papers are at ICML 2026?

ICML 2026 has 604 Deep learning · generative models and autoencoders papers, versus 275 the year before.

How many Deep learning · generative models and autoencoders papers are at CVPR 2026?

CVPR 2026 has 174 Deep learning · generative models and autoencoders papers, versus 110 the year before.

Which conference publishes the most Deep learning · generative models and autoencoders papers?

NeurIPS, with 1,714 papers from 2023 to 2026.