SelfEval: Leveraging the discriminative nature of generative models for
  evaluation
In this work, we show that text-to-image generative models can be 'inverted'
to assess their own text-image understanding capabilities in a completely
automated manner.
  Our method, called SelfEval, uses the generative model to compute the
likelihood of real images given text prompts, making the generative model
directly applicable to discriminative tasks.
  Using SelfEval, we repurpose standard datasets created for evaluating
multimodal text-image discriminative models to evaluate generative models in a
fine-grained manner: assessing their performance on attribute binding, color
recognition, counting, shape recognition, spatial understanding.
  To the best of our knowledge SelfEval is the first automated metric to show a
high degree of agreement for measuring text-faithfulness with the gold-standard
human evaluations across multiple models and benchmarks.
  Moreover, SelfEval enables us to evaluate generative models on challenging
tasks such as Winoground image-score where they demonstrate competitive
performance to discriminative models.
  We also show severe drawbacks of standard automated metrics such as
CLIP-score to measure text faithfulness on benchmarks such as DrawBench, and
how SelfEval sidesteps these issues.
  We hope SelfEval enables easy and reliable automated evaluation for diffusion
models.
655ac665deee83130a470af9
Sai Saketh Rambhatla
admin_assigned
2023-11-20T09:29:38.103Z
6435a89b876d46610bf30b21
/avatars/88dba6589b07ad310bad94108774d893.svg
Saketh Rambhatla
user
rssaketh
655ac665deee83130a470afa
Ishan Misra
SelfEval: Leveraging the discriminative nature of generative models for
  evaluation
In this work, we show that text-to-image generative models can be 'inverted'
to assess their own text-image understanding capabilities in a completely
automated manner.
  Our method, called SelfEval, uses the generative model to compute the
likelihood of real images given text prompts, making the generative model
directly applicable to discriminative tasks.
  Using SelfEval, we repurpose standard datasets created for evaluating
multimodal text-image discriminative models to evaluate generative models in a
fine-grained manner: assessing their performance on attribute binding, color
recognition, counting, shape recognition, spatial understanding.
  To the best of our knowledge SelfEval is the first automated metric to show a
high degree of agreement for measuring text-faithfulness with the gold-standard
human evaluations across multiple models and benchmarks.
  Moreover, SelfEval enables us to evaluate generative models on challenging
tasks such as Winoground image-score where they demonstrate competitive
performance to discriminative models.
  We also show severe drawbacks of standard automated metrics such as
CLIP-score to measure text faithfulness on benchmarks such as DrawBench, and
how SelfEval sidesteps these issues.
  We hope SelfEval enables easy and reliable automated evaluation for diffusion
models.
SelfEval automates the assessment of text-image understanding in generative models by computing image likelihoods given text prompts, showing competitive performance to discriminative models on fine-grained tasks.
SelfEval generative models discriminative tasks attribute binding color recognition counting shape recognition spatial understanding Winoground CLIP-score DrawBench diffusion models