Paper Citation Record · LEDGER

Reward Incremental Learning in Text-to-Image Generation

As of 13 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2411.17310.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.17310 v1

Coverage vector

measured 42 of 42 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation 20bd969f-e38d-4fd3-b061-4d904e7d93bd · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

Reward Incremental Learning in Text-to-Image Generation Training Diffusion Models with Reinforcement Learning

Reference 1

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Observation 67f9b7fe-e182-4552-b184-e50b8287c247 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Reward Incremental Learning in Text-to-Image Generation Emerg- ing properties in self-supervised vision transformers

Reference 2

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This paper cites Riemannian walk for incremen- tal learning: Understanding forgetting and intransigence.

Reward Incremental Learning in Text-to-Image Generation Riemannian walk for incremen- tal learning: Understanding forgetting and intransigence

Reference 3

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This paper cites Lifelong machine learning.

Reward Incremental Learning in Text-to-Image Generation Lifelong machine learning

Reference 4

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Observation 43dd8393-47a3-4885-ac5d-cd2997916a5f · outbound

This paper cites Directly fine-tuning diffusion models on differentiable re- wards.

Reward Incremental Learning in Text-to-Image Generation Directly fine-tuning diffusion models on differentiable re- wards

Reference 5

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Observation 9485af76-6199-47d5-8d5b-a93503813702 · outbound

This paper cites A continual learning survey: Defying forgetting in classification tasks.

Reward Incremental Learning in Text-to-Image Generation A continual learning survey: Defying forgetting in classification tasks

Reference 6

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Observation 0a9253f3-5fed-484f-b5a3-a0635b8d6342 · outbound

This paper cites Cogview: Mastering text-to-image gen- eration via transformers.

Reward Incremental Learning in Text-to-Image Generation Cogview: Mastering text-to-image gen- eration via transformers

Reference 7

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Observation c2e759b1-98ba-49c8-8d1e-b111909da514 · outbound

This paper cites An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks.

Reward Incremental Learning in Text-to-Image Generation An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

Reference 8

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Observation 2aa303a0-e060-4a97-bc2b-2647e1ef1c10 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level perfor- mance on imagenet classification.

Reward Incremental Learning in Text-to-Image Generation Delving deep into rectifiers: Surpassing human-level perfor- mance on imagenet classification

Reference 9

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Observation 34e74fc7-bf56-4e49-9aaa-584711dd3c19 · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

Reward Incremental Learning in Text-to-Image Generation Momentum contrast for unsupervised visual rep- resentation learning

Reference 10

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Observation 04b57597-d8ab-4574-a90d-33228869f298 · outbound

This paper cites Clipscore: A reference-free evaluation met- ric for image captioning.

Reward Incremental Learning in Text-to-Image Generation Clipscore: A reference-free evaluation met- ric for image captioning

Reference 11

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Observation df664039-0f73-44a3-b3d7-2b033d7de1c9 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.

Reward Incremental Learning in Text-to-Image Generation Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 12

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Observation 847eca63-3c7c-4da4-9de3-c2bff3d12ce4 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Reward Incremental Learning in Text-to-Image Generation Denoising dif- fusion probabilistic models

Reference 13

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This paper cites Re-evaluating continual learning scenarios: A cate- gorization and case for strong baselines.

Reward Incremental Learning in Text-to-Image Generation Re-evaluating continual learning scenarios: A cate- gorization and case for strong baselines

Reference 14

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Observation af5fe0fc-d031-4884-9eae-94ebabe3adaf · outbound

This paper cites Aligning Text-to-Image Models using Human Feedback.

Reward Incremental Learning in Text-to-Image Generation Aligning Text-to-Image Models using Human Feedback

Reference 15

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This paper cites Inflora: Interference-free low-rank adaptation for continual learning.

Reward Incremental Learning in Text-to-Image Generation Inflora: Interference-free low-rank adaptation for continual learning

Reference 16

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Observation f172894a-b926-4079-9cfb-5b0689d097a2 · outbound

This paper cites Online continual learning in image classification: An empirical survey.

Reward Incremental Learning in Text-to-Image Generation Online continual learning in image classification: An empirical survey

Reference 17

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This paper cites Catastrophic inter- ference in connectionist networks: The sequential learning problem.

Reward Incremental Learning in Text-to-Image Generation Catastrophic inter- ference in connectionist networks: The sequential learning problem

Reference 18

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This paper cites Rethinking momentum knowledge distillation in online continual learning.

Reward Incremental Learning in Text-to-Image Generation Rethinking momentum knowledge distillation in online continual learning

Reference 19

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This paper cites Exponential moving average of weights in deep learn- ing: Dynamics and benefits.

Reward Incremental Learning in Text-to-Image Generation Exponential moving average of weights in deep learn- ing: Dynamics and benefits

Reference 20

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This paper cites Ava: A large-scale database for aesthetic visual analysis.

Reward Incremental Learning in Text-to-Image Generation Ava: A large-scale database for aesthetic visual analysis

Reference 21

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Observation d6e18d93-2ae7-457a-822a-636fbb53b461 · outbound

This paper cites Aligning Text-to-Image Diffusion Models with Reward Backpropagation.

Reward Incremental Learning in Text-to-Image Generation Aligning Text-to-Image Diffusion Models with Reward Backpropagation

Reference 22

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This paper cites Simulacra aesthetic captions.

Reward Incremental Learning in Text-to-Image Generation Simulacra aesthetic captions

Reference 23

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Observation 896e9c27-d6d8-4c78-b880-5cc95bdc2a21 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

Reward Incremental Learning in Text-to-Image Generation Learn- ing transferable visual models from natural language super- vision

Reference 24

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This paper cites Zero-shot text-to-image generation.

Reward Incremental Learning in Text-to-Image Generation Zero-shot text-to-image generation

Reference 25

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This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Reward Incremental Learning in Text-to-Image Generation Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 26

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This paper cites icarl: Incremental classi- fier and representation learning.

Reward Incremental Learning in Text-to-Image Generation icarl: Incremental classi- fier and representation learning

Reference 27

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This paper cites High-resolution image syn- thesis with latent diffusion models.

Reward Incremental Learning in Text-to-Image Generation High-resolution image syn- thesis with latent diffusion models

Reference 28

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This paper cites LAION- aesthetics.

Reward Incremental Learning in Text-to-Image Generation LAION- aesthetics

Reference 29

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Observation bdebd1e8-9af4-43fb-8f9f-eb916796faf9 · outbound

This paper cites Jpeg-resistant adversarial im- ages.

Reward Incremental Learning in Text-to-Image Generation Jpeg-resistant adversarial im- ages

Reference 30

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This paper cites Denoising Diffusion Implicit Models.

Reward Incremental Learning in Text-to-Image Generation Denoising Diffusion Implicit Models

Reference 31

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This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.

Reward Incremental Learning in Text-to-Image Generation Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 32

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This paper cites Three scenarios for continual learning.

Reward Incremental Learning in Text-to-Image Generation Three scenarios for continual learning

Reference 33

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This paper cites A comprehensive survey of continual learning: theory, method and application.

Reward Incremental Learning in Text-to-Image Generation A comprehensive survey of continual learning: theory, method and application

Reference 34

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Observation e24bd065-4a3a-40b3-a0b7-f3fb9599204a · outbound

This paper cites Investigating Prompt Engineering in Diffusion Models.

Reward Incremental Learning in Text-to-Image Generation Investigating Prompt Engineering in Diffusion Models

Reference 35

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This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing in- ference time.

Reward Incremental Learning in Text-to-Image Generation Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing in- ference time

Reference 36

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Observation 03bbd130-9a7e-4cd9-9d9e-15c8e1662f98 · outbound

This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

Reward Incremental Learning in Text-to-Image Generation Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

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This paper cites Imagere- ward: Learning and evaluating human preferences for text- to-image generation.

Reward Incremental Learning in Text-to-Image Generation Imagere- ward: Learning and evaluating human preferences for text- to-image generation

Reference 38

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This paper cites Towards language-free training for text-to-image generation.

Reward Incremental Learning in Text-to-Image Generation Towards language-free training for text-to-image generation

Reference 39

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Reward Incremental Learning in Text-to-Image Generation Unresolved cited work

Reference 40

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This paper cites Apart from the RIL set- ting, we also compare results with a jointly trained approach that optimizes the weighted sum of three reward objectives using the adapted baseline.

Reward Incremental Learning in Text-to-Image Generation Apart from the RIL set- ting, we also compare results with a jointly trained approach that optimizes the weighted sum of three reward objectives using the adapted baseline

Reference 41

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This paper cites animation.

Reward Incremental Learning in Text-to-Image Generation animation

Reference 42

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